Notices for files:
Copyright 2008-2011 Google Inc.
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.
Notices for files:
- android-query-full.0.26.7.jar
Code license
Apache License 2.0
Notices for Project:
The code in this project is licensed under the Apache Software License 2.0, per the terms of the included LICENSE file.
Copyright (c) 2008-2009 CommonsWare, LLC
Portions (c) 2009 Google, Inc.
Notices for Project:
Licensed under the Apache License, Version 2.0
Copyright (c) 2012 Fabian Leon
Notices for files:
Copyright (C) 2011 The Android Open Source Project
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.
Notices for files:
- okhttp-2.0.0.jar
- okhttp-urlconnection-2.0.0.jar
- okio-1.0.1.jar
Copyright 2014 Square, Inc.
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
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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.


national institutes health
shows 126 millions
adultes live
PAIN RELIEF FORMULA
DETOXIFY
liver and improve over all well being
TREAT
Anaxiety & Depression without side effects
RELIEF
from chronic pain & inflamation
PROMOTES
improved sleep patterns
Research from the national institutes of health shows that 126 millions US adultes live with cronic pain Research from the national institutes of health shows that 126 millions US adultes live with cronic painResearch from the national institutes of health shows that 126 millions
ARE YOU READY TO FEEL GOOD AGAIN?
national institutes health
shows 126 millions
adultes live
national institutes health
shows 126 millions
adultes live
national institutes health
shows 126 millions
adultes live
national institutes health
shows 126 millions
adultes live
NATURAL PAIN RELIEF CAPSULE
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sint architecto
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aut voluptatibus consequatur facilis assumenda.
dolor sit amet consectetur
adipisicing elit.
Explicabo, aperiam
omnis, ullam hic similique
sint architecto
Lorem, ipsum dolor sit amet consectetur adipisicing elit. Rerum perferendis odit at.
Obcaecati
commodi libero voluptatum corporis nesciunt tempore
DETOXIFY
liver and improve over all well being
TREAT
Anaxiety & Depression without side effects
RELIEF
from chronic pain & inflamation
PROMOTES
improved sleep patterns


pymatgen.analysis.wulff module¶
This module define a WulffShape class to generate the Wulff shape from a lattice, a list of indices and their corresponding surface energies, and the total area and volume of the wulff shape,the weighted surface energy, the anisotropy and shape_factor can also be calculated. In support of plotting from a given view in terms of miller index.
The lattice is from the conventional unit cell, and (hkil) for hexagonal lattices.
If you use this code extensively, consider citing the following:
Tran, R.; Xu, Z.; Radhakrishnan, B.; Winston, D.; Persson, K. A.; Ong, S. P. (2016). Surface energies of elemental crystals. Scientific Data.
-
class
WulffFacet
(normal, e_surf, normal_pt, dual_pt, index, m_ind_orig, miller)[source]¶ Bases:
object
Helper container for each Wulff plane.
-
class
WulffShape
(lattice, miller_list, e_surf_list, symprec=1e-05)[source]¶ Bases:
object
Generate Wulff Shape from list of miller index and surface energies, with given conventional unit cell. surface energy (Jm^2) is the length of normal.
Wulff shape is the convex hull. Based on: http://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html
- Process:
- get wulff simplices
- label with color
- get wulff_area and other properties
-
debug
(bool)¶
-
color_set
¶
-
grid_off
(bool)¶
-
axis_off
(bool)¶
-
show_area
¶
-
off_color
¶ -
color of facets off wulff
-
structure
¶ -
Structure object, input conventional unit cell (with H ) from lattice
-
miller_list
¶ -
list of input miller index, for hcp in the form of hkil
-
hkl_list
¶ -
modify hkill to hkl, in the same order with input_miller
-
e_surf_list
¶ -
list of input surface energies, in the same order with input_miller
-
lattice
¶ -
Lattice object, the input lattice for the conventional unit cell
-
facets
¶ -
[WulffFacet] for all facets considering symm
-
dual_cv_simp
¶ -
simplices from the dual convex hull (dual_pt)
-
wulff_pt_list
¶
-
wulff_cv_simp
¶ -
simplices from the convex hull of wulff_pt_list
-
on_wulff
¶ -
list for all input_miller, True is on wulff.
-
color_area
¶ -
list for all input_miller, total area on wulff, off_wulff = 0.
-
miller_area
¶ -
($hkl$): area for all input_miller
Parameters: - lattice – Lattice object of the conventional unit cell
- miller_list ([(hkl) – list of hkl or hkil for hcp
- e_surf_list ([float]) – list of corresponding surface energies
- symprec (float) – for recp_operation, default is 1e-5.
-
anisotropy
¶ (float) Coefficient of Variation from weighted surface energy The ideal sphere is 0.
Type: Returns
-
area_fraction_dict
¶ {hkl: area_hkl/total area on wulff}
Type: Returns Type: (dict)
-
effective_radius
¶ Radius of the Wulffshape when the Wulffshape is approximated as a sphere.
Returns: (float) radius.
-
get_plot
(color_set='PuBu', grid_off=True, axis_off=True, show_area=False, alpha=1, off_color='red', direction=None, bar_pos=(0.75, 0.15, 0.05, 0.65), bar_on=False, units_in_JPERM2=True, legend_on=True, aspect_ratio=(8, 8), custom_colors={})[source]¶ Get the Wulff shape plot.
Parameters: - color_set – default is ‘PuBu’
- grid_off (bool) – default is True
- axis_off (bool) – default is Ture
- show_area (bool) – default is False
- alpha (float) – chosen from 0 to 1 (float), default is 1
- off_color – Default color for facets not present on the Wulff shape.
- direction – default is (1, 1, 1)
- bar_pos – default is [0.75, 0.15, 0.05, 0.65]
- bar_on (bool) – default is False
- legend_on (bool) – default is True
- aspect_ratio – default is (8, 8)
- ({(h,k,l} (custom_colors) – [r,g,b,alpha}): Customize color of each facet with a dictionary. The key is the corresponding Miller index and value is the color. Undefined facets will use default color site. Note: If you decide to set your own colors, it probably won’t make any sense to have the color bar on.
Returns: (matplotlib.pyplot)
-
miller_area_dict
¶ area_hkl on wulff}
Type: Returns {hkl
-
miller_energy_dict
¶ surface energy_hkl}
Type: Returns {hkl
-
shape_factor
¶ This is useful for determining the critical nucleus size. A large shape factor indicates great anisotropy. See Ballufi, R. W., Allen, S. M. & Carter, W. C. Kinetics
of Materials. (John Wiley & Sons, 2005), p.461Returns: (float) Shape factor.
-
show
(*args, **kwargs)[source]¶ Show the Wulff plot.
Parameters: - *args – Passed to get_plot.
- **kwargs – Passed to get_plot.
-
surface_area
¶ Total surface area of Wulff shape.
-
tot_corner_sites
¶ Returns the number of vertices in the convex hull. Useful for identifying catalytically active sites.
-
tot_edges
¶ Returns the number of edges in the convex hull. Useful for identifying catalytically active sites.
-
total_surface_energy
¶ Total surface energy of the Wulff shape.
Returns: (float) sum(surface_energy_hkl * area_hkl)
-
volume
¶ Volume of the Wulff shape
-
weighted_surface_energy
¶ sum(surface_energy_hkl * area_hkl)/ sum(area_hkl)
Type: Returns


AngularJS 202
@mgonto
We know all the basics
We're learning some advanced topics
So, how does this all work?
Concepts
- Javascript is a "turn based game"
- JS code is called via a call stack
- $digest is a wrapper or decorator for regular function execution
- $apply performs a $digest
- Thumb rule: Call $apply when an event/function that's not known by AngularJS is called
Then, what's a template?
Markup

My name is {{person.name | firstName}}

<alert-button msg="'Cool message'"/>

Watching in JS
$scope.personName = null;
$scope.$watch('person.name', function(name) {
if (!name) return;
$scope.personName = $filter('firstName')(name);
});
// Or
$scope.$watch('person.name | firstName', function(name) {
if (!name) return;
$scope.personName = name;
});
Different watchers
$scope.person = {name: "gonto"};
$scope.$watch('person', function(newValue, oldValue) {
if (!newValue) return;
// Only called on reference change
});
$scope.$watch('person', function(newValue, oldValue) {
if (!newValue) return;
// Called on value changed
}, true);


VideoCV is an online career site. The service platform provides innovative online recruitment tools for companies and jobseekers, making video job posting and finding a job more presentable ,interactive, easy and efficient. Registered companies can post job postings which jobseekers can then find and apply for. Under no circumstances are we involved in any way in the selection process of the company. Every company uses its own processes and procedures. In order to take full advantage of our services, employers and jobseekers must first register on our website.
In order to take full advantage of our services, employers and jobseekers must first register on our website.
From your Account you can see at a glance your latest applications, your default video Resume, etc. Your Account allows you to see an overview of your activities on the site.
f you have found an interesting posting and believe that your resume fits with the company's requirements, then you can apply for it by sending them your video resume & Document (.doc, .pdf etc) resume.
You can check which stage in the selection process your application has reached by going to "My Account". Under "My Applications", you will then see one of the following labels next to each entry :
If you have forgotten your password you can ask us to resend it to you by email from the videoresume.com "Sign in" page. Just below the boxes where you enter your email and password you will find the link "Did you forget your password?.
If you have forgotten your password you can ask us to resend it to you by email from the videoresume.com "Sign in" page. Just below the boxes where you enter your email and password you will find the link "Did you forget your password?.
Companies carry out their recruitment processes according to their own internal policies. Videoocv.com is only responsible for posting the job vacancies and maintaining the database containing candidate resumes. We have nothing to do with the company's internal recruitment processes. You can find the status of your applications in your dashboard (My Account).
Some companies contact all the applicants by telephone or email, whether they intend to interview them or not. Others only contact those they have selected as being "Finalists". However, even then, not all companies actually make contact with all the finalists.
If you have registered with videooc.com but you haven't applied for any positions posted by companies you will most likely not receive many job offers. You have to apply for a position that has been posted in order for the company to know that you are interested in the vacancy.
On the Videoocv.com home page, you will see a search box where you can specify the fields for your search (by entering a keyword and the state where you want to work). Once you have done a search, select the posting(s) you are interested in. To send your video Resume to a company, click on "Apply for this position". The system will ask you to enter your email address and password. You can then select the Resume you wish to send along with a Cover Letter (optional). The company will receive your application and then contact you should it wish to continue with the process.
You can edit the video resume any point of time without any hassle by entering “my account “and upload new video resume in place of existing video resume.
Yes your video resume is safe and secured.
At Videocv.com you can register and enjoy the following services:
From the videoocv.com click on "Employer post a job". Carefully complete the registration form . The fields marked with a red asterisks are compulsory
Once you have completed the process you are then registered and can begin posting vacancies. However, these won't appear on the page until you have received a telephone call from the videoocv.com Customer Service department to validate your registration details. This process is essential in order to assure our candidates that the companies offering employment are reliable
This process only needs to be done once. Once your company details have been finished the registration process you will be able to access My Account using your email and password. Here you can change your login details, edit, modify or delete job postings, and view the resumes of the candidates who have responded to your vacancies.
Click on the "Sign In" link and enter your email and password to access My Account.
SIn My Account you will see a list of all the vacancies you have posted. At the top of the page, you will find a number of boxes that enable you to filter the postings (active, inactive, draft or all).
Each offer is shown on a separate row in the list. Clicking on "Edit" will open the offer form and allow you to change any fields you choose. Remember, however, that you are unable to modify the "Position", "Country" or "Category" fields.
When you have made the changes you require, simply click on "Save". Your offer will now be updated with any changes you made.
When a candidate applies for your job vacancy, you will receive the application in your private account.
We recommend that you manage the applications you have received by moving them to the appropriate folder (processed, rejected, or finalist) so that you can see at a glance what candidates are in which stage of the selection process. By moving the applications to the corresponding folders, you also provide information to the candidate who is monitoring the status is the application. If you move someone to the Finalist folder s/he will expect you to contact him/her by e-mail or telephone.
You can contact candidates directly by using the data contained in the application.
When reviewing the resumes received in response to an offer, you will see a list of all the candidates and three columns showing their professional experience, education and place of residence. If you select any of these elements, the system will filter the candidates to only show the details you have specified.The filter works in real time and enables you to quickly view specific details for the candidates you are most interested in .
An offer can be posted for 60 days, although this period can also be prolonged if required. When this time expires the offer is autoarchived as "Inactive" and will not be visible to candidates.
If you wish to stop your postings from being removed from the website, you can do this in the 5-day period prior to their expiry date. If you edit the posting during this period, you will have the option to "Renew". Selecting this option will mean your postings will be visible for another 60 days.
In the "My job postings" section select the icon "Edit" for the posting you wish to renew.
No Videoocv.com is only a dedicated recruitment facilitating portal , where candidates background checks are not done.


Measurable Function
INTRO. Measurable functions in measure theory are analogous to continuous functions
in topology. A continuous function pulls back open sets to open sets, while a
measurable function pulls back measurable sets to measurable sets.
INTRO. The measurability of a function depends only on the \(\sigma\)-algebra;
it is not necessary that any measures are defined. So, very often, measurable functions are defined
between measurable space (not measure space).
INTRO. In order to show that a function is measurable, it is sufficient to check the
measurability of the inverse images of sets that generate the \(\sigma\)-algebra on the target space.


Sorry, can't get the Team Info right now.
Loading Team Info...
The team {{ team.name }} has {{ team.count }} team members.
Shout-Out:
Bot has Shouted Out to {{ bot.shoutOutCount }} team members.


Hello world
test paste from OpenOffice
test
innerText:
getInnerText:
inner text (stringified):
text content (stringified):


INTRODUCTION
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Forest of the Lost [aq57], forest, in Imperia, peasants: 140
Last updated Turn #126
Forest of the Lost [aq57], forest, in Imperia, peasants: 140
------------------------------------------------------------------------
5: Pack of wolves [hz9v], wolves, number: 7, went east.
19: Stack of nazgul [is3t], nazgul, number: 3, arrived from the east.
21: Stack of nazgul [is3t], nazgul, number: 3, went west.
24: Stack of nazgul [dt6y], nazgul, number: 2, arrived from the east.
26: Stack of nazgul [dt6y], nazgul, number: 2, went west.
30: The population grows by one peasant.
Routes leaving Forest of the Lost:
North, forest, to Forest of the Lost [ap57], 8 days
East, forest, to Forest of the Lost [aq58], 8 days
South, to Ocean [ar57], The Inner Sea, 2 days
West, forest, to Forest of the Lost [aq56], 8 days
Secret route, to Yew grove [ad8a], hidden, 1 day
It is raining.
Seen here:
Stack of nazgul [gg0a], nazgul, number: 4


Florida faces Buffalo, seeks 4th straight home win
by Associated PressBuffalo Sabres (10-9-3, sixth in the Atlantic Division) vs. Florida Panthers (12-6-5, second in the Atlantic Division)
Sunrise, Florida; Sunday, 5 p.m. EST
BOTTOM LINE: Florida hosts Buffalo trying to prolong its three-game home winning streak.
The Panthers are 3-1-1 in division matchups. Florida ranks second in the league recording 9.9 points per game, averaging 3.6 goals and 6.3 assists.
The Sabres are 4-3-0 against the rest of their division. Buffalo has scored 15 power-play goals, converting on 20.8% of chances.
Buffalo defeated Florida 3-2 in the last meeting between these teams on Oct. 11.
TOP PERFORMERS: Keith Yandle leads the Panthers with a plus-seven in 23 games played this season. Jonathan Huberdeau has totaled 12 assists over the last 10 games for Florida.
Jack Eichel has collected 26 total points while scoring 13 goals and totaling 13 assists for the Sabres. Victor Olofsson has recorded five assists over the last 10 games for Buffalo.
LAST 10 GAMES: Sabres: 1-7-2, averaging two goals, 3.1 assists, 3.9 penalties and 10 penalty minutes while giving up 3.4 goals per game with a .888 save percentage.
Panthers: 6-3-1, averaging 3.7 goals, 6.2 assists, 2.8 penalties and 5.9 penalty minutes while giving up 3.2 goals per game with a .902 save percentage.
Panthers Injuries: Jayce Hawryluk: out (upper body).
Sabres Injuries: Johan Larsson: out (upper body).
___
The Associated Press created this story using technology provided by Data Skrive and data from Sportradar.


{% block subject %} {% block trans %}Account activation on Alx-accessor {% endblock trans %} {% endblock subject %} {% block text_body %} You're receiving this email because you need to finish the activation process on "Alx-accessor" Please go to the following page to activate your account: https://alx-accessor.netlify.app/activate/{{uid}}/{{token}} Thanks for joining us. {% endblock text_body %}


| 011:048 Khan | : |
It was said: "O Nuh (Noah)! Come down (from the ship) with peace from Us and blessings on you and on the people who are with you (and on some of their off spring), but (there will be other) people to whom We shall grant their pleasures (for a time), but in the end a painful torment will reach them from Us." |
| 011:048 Maulana | : |
It was said: O Noah, descend with people from Us and blessings on thee and on nations (springing) from those with thee. And there are nations whom We afford provisions, then a painful punishment from Us afflicts them. |
| 011:048 Pickthal | : |
It was said (unto him): O Noah! Go thou down (from the mountain) with peace from Us and blessings upon thee and some nations (that will spring) from those with thee. (There will be other) nations unto whom We shall give enjoyment a long while and then a painful doom from Us will overtake them. |
| 011:048 Rashad | : |
It was proclaimed: "O Noah, disembark, with peace and blessings upon you, and upon nations who will descend from your companions. As for the other nations descending from you, we will bless them for awhile, then commit them to painful retribution." |
| 011:048 Sarwar | : |
Noah was told, "Get down from the Ark.   |
| 011:048 Shakir | : |
It was said: O Nuh! descend with peace from Us and blessings on you and on the people from among those who are with you, and there shall be nations whom We will afford provisions, then a painful punishment from Us shall afflict them. |
| 011:048 Sherali | : |
It was said, 'O Noah, descend then with peace from US and blessings upon thee and upon peoples to be born of those with thee. And there will be other peoples whom WE shall grant provision for a time, then shall a grievous punishment overtake them from US.' |
| 011:048 Yusufali | : |
The word came: "O Noah! Come down (from the Ark) with peace from Us, and blessing on thee and on some of the peoples (who will spring) from those with thee: but (there will be other) peoples to whom We shall grant their pleasures (for a time), but in the end will a grievous penalty reach them from Us." |
Concepts/Themes Covered:
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


{% extends 'layout.html' %} {% block pageTitle %} State Pension Activation {% endblock %} {% block content %} {% include 'includes/parts/phase-banner.html' %} Your personal data The only data GOV.UK Verify will share with the Department for Work and Pensions is: your name your date of birth your address Can we use this information when you claim your State Pension? Yes No {% endblock %} {% block pageScripts %} {% include 'includes/scripts/ninoFocus.html' %} {% include 'includes/scripts/dobFocus.html' %} {% include 'sprint9/scripts/clear_fields.html' %} {% endblock %}
The only data GOV.UK Verify will share with the Department for Work and Pensions is:


Todos são capazes de compartilhar várias receitas de um mesmo prato de comida.
Você pode colocar nests site as receitas que você quer que todos saibam.
Aprenda os melhores pratos para ser o melhor dos chefes de cozinha
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Involvement of tyrosine phosphorylation in endothelial adhesion molecule induction.
Abstract
Induction of endothelial adhesion molecules by the cytokine tumor necrosis factor-alpha (TNF) can occur independently of protein kinase C and activation of a protein tyrosine kinase (PTK) has recently been implicated in the upregulation of vascular cell adhesion molecule 1 (VCAM-1) by interleukin-4 (IL-4) on endothelial cells. We demonstrate that the PTK inhibitors herbimycin A or genistein suppress induction of endothelial VCAM-1 and E-selectin, as well as subsequent monocytic cell adhesion to endothelial cells stimulated by TNF. Inhibition studies indicate that specific tyrosine phosphorylation following PTK activation is involved in the mobilization of the transcription factor, nuclear factor kappa B, and VCAM-1 mRNA expression. This may have implications for pathophysiological conditions that involve the upregulation of these molecules (e.g. inflammation and atherosclerosis).


One Of My Story
When I was in middle school, I have some best friends. We alwasys
go out on friday night and have fun together during the weekend.
As time goes by, we go to high school but we are not in the same high school.
The connection between us is getting less and less.
Druing the high school, we can meet each other once in a few months.
But I never think that the brotherhood between us are decreased.
Now, I study in the United States, all of my best friends are in China.
We can hardly meet maybe 1 or 2 years. But everytime I go back to my hometown,
we always hungout just like what we do in the middle school. I really miss my best friedns.
Next year, one of my best firend is going to get married with his lover, we really feel happy for him.
I told them this winter holiday that I will come to the wedding ceremony no matter how busy I am.
It seems all of my best friends have their girlfriends, I really feel happy for them.
And also, I think I need to work hard to find my girlfriend. LOL.
Photo
Image credit: Yi Man.
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lnewusers
Section: User Commands (1)Updated: Jul 13 2008
Index Return to Main Contents
NAME
lnewusers - Create new user accountsSYNOPSIS
lnewusers [OPTION]...DESCRIPTION
Creates new user accounts using data read from standard input.The input data consists of lines, each line has 7 colon-separated fields:
- User name
-
- Plaintext password
-
- User ID
-
lnewusers
refuses to create users with user ID 0 (the root user ID).
- Group
-
If this field is a valid group ID,
it is interpreted as a group ID,
otherwise as a group name.
If the field is empty,
a group name equal to user name is used.
If the specified group does not exist, it is automatically created. If the group is specified with a group ID, the created group has group name equal to the user name.
- SM]GECOS
-
The SM]GECOS field is traditionally used to store user's real name
and other information.
- Home directory
-
If this field is empty,
a default specified by
libuser
configuration,
or /home/username if
libuser
configuration does not specify a default,
is used.
- Login shell
-
If this field is empty,
a default specified by
libuser
configuration is used.
Errors in user specifications are reported and processing continues on the next line.
OPTIONS
- -f, --file=file
-
Read account data from file instead of standard input.
- -i, --interactive
-
Ask all questions when connecting to the user database,
even if default answers are set up in
libuser
configuration.
- -M, --nocreatehome
-
Don't create home directories.
- -n, --nocreatemail
-
Don't create mail spool files.
EXIT STATUS
The exit status is 0 on success, nonzero on fatal error. Errors in user specifications are not reflected in the exit status.
Index
This document was created by man2html, using the manual pages.
Time: 05:29:05 GMT, December 24, 2015


In this example I've created a simple Angular.js app which submits a user's zip code via http request to The Weather Channel's free online API. This returns a JSON object with a ton of useful information we can access and repurpose for use in maps layers or weather forecasting, including temperature, precipitation, wind, even latitude and longitude. The raw output is below.
{{ georesponse | json }}


| Meeting Time: | 7:00am |
| Meeting Location: | Jekyll Island Public Parking Lot 549 Beachview Dr N Jekyll Island, GA 31527 |
| On-Site Supervisor: | Orlando Johnson |
| Meeting Time: | 9:00am |
| Meeting Location: | Chatham Ave. Beach Access 1811 Chatham Ave Tybee Island, GA 31328 |
| On-Site Supervisor: | Brian Wilson |
| Meeting Time: | 7:00am |
| Meeting Location: | Myrtle Beach Parking Lot 1406 Withers Dr Myrtle Beach, SC 29577 |
| On-Site Supervisor: | Nathan Ward |
| Meeting Time: | 10:00am |
| Meeting Location: | East Beach Parking Lot 4202 1st St St Simons, GA, 31522 |
| On-Site Supervisor: | Chase LeDoux |
| Meeting Time: | 8:00am |
| Meeting Location: | Driftwood Beach Parking Lot 157 N Loop Trail Jekyll Island, GA 31527 |
| On-Site Supervisor: | Kenneth Sweet |
| Meeting Time: | 7:00am |
| Meeting Location: | Gould's Inlet Parking Lot 564 Bruce Drive Jekyll Island, GA 31527 |
| On-Site Supervisor: | Katelyn Dever |
| Meeting Time: | 9:00am |
| Meeting Location: | Garden City Beach Public Parking Lot 115 S Waccamaw Dr Murrells Inlet, SC 29576 |
| On-Site Supervisor: | Orlando Johnson |
| Meeting Time: | 7:00am |
| Meeting Location: | Jekyll Island Public Parking Lot 549 Beachview Dr N Jekyll Island, GA 31527 |
| On-Site Supervisor: | Nathan Ward |
| Meeting Time: | 7:00am |
| Meeting Location: | Jekyll Island Public Parking Lot 549 Beachview Dr N Jekyll Island, GA 31527 |
| On-Site Supervisor: | Brian Wilson |
All ages are welcome! Under 13 must be accompanied by a parent or gaurdian. We are not responsible or liable for accidents. Please volunteer at your own risk.
What should I bring with me to volunteer?A refillable water bottle, a hat, reef-safe sunscreen, and yourself! Gloves, reclycling bags, water, and a small snack will be provided!
What should I wear?Dress comfortably! We work hard and it can be hot outside. Swimsuits can be worn under a tshirt and shorts or a cover up, please be respectful to other volunteers with these choices. Please wear a hat and sunscreen to prevent burns. We STRONGLY encourage the use of reef-safe sunscreen products!
What time do I need to arrive on the site, and how long will we work?Be there by the time on the event details above! At this time, we will begin a 15 minute orientation, pass out supplies, and set goals for our day. After starting, we will work for 2.5 hours on a typical day. Some volunteers choose to stay longer or leave early. You are free to do either!


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Proactively streamline world-class experiences without front-end networks. Progressively utilize quality e-services via front-end processes. Conveniently re-engineer client-based mindshare vis-a-vis optimal synergy.


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Former home secretary ‘left slack-jawed by Andrew’s racist comments’
Jacqui Smith, who served at the Home Office under Tony Blair, said the duke made racist comments about Arab people during a state dinner.
A former home secretary has said she was left “slack-jawed” by alleged racist comments by the Duke of York.
Jacqui Smith, who served at the Home Office under Tony Blair, said Andrew made racist comments about Arab people during a state dinner.
The claims come after the duke’s Newsnight interview in which he discussed his friendship with convicted sex offender Jeffrey Epstein and denied sleeping with one of Epstein’s victims, Virginia Giuffre.
Speaking on the LBC election podcast she hosts with Iain Dale, Ms Smith said Andrew’s remarks to her “involved a comment about camels” and were “as worse as you could imagine”.
Ms Smith said: “I met him several times, including once at a state banquet where, after dinner, I and my husband and another Labour cabinet minister had a drink with him.
“I have to say the conversation left us slack-jawed with the things that he felt it was appropriate to say.”
The former Labour politician made the comments in Sunday’s podcast episode, and said she did not feel comfortable repeating what the duke said, adding: “It was a state dinner for the Saudi royal family and he made racist comments about Arabs that were unbelievable.”
Describing Andrew as “the worst end of the royal family”, Ms Smith added: “The fact he thought we might find these amusing was sort of a terrible situation to be in.
“I don’t think he’s very bright, I don’t think he really understands the way in which he behaves.”
She also said she feels “ashamed” that she did not challenge the Queen’s son on his comments.
Responding to the claims, a Buckingham Palace spokesman said: “HRH has undertaken a considerable amount of work in the Middle East over a period of years and has many friends from the region.
“He does not tolerate racism in any form.”
On Monday, the Evening Standard published on its front page a claim by columnist Rohan Silva that the duke made a racist remark while he was a Downing Street aide specialising in the tech economy.
Mr Silva told the newspaper that when he asked Andrew if the government department responsible for trade “could be doing a better job”, the duke replied: “Well, if you’ll pardon the expression, that really is the n***** in the woodpile.”
Sources have categorically denied Andrew used the word.


Examples CSS Selector Used to Test Basic CSS Selectors
Selenium tests have been written to verify CSS-Selectotrs-To-Xpath functionality
Both CSS Selectors and XPaths generated by calling converCssStringToXpahString() are used to validate the software.


|
OCILIB (C and C++ Driver for Oracle)
4.5.1
|
Oracle Transaction object. More...
#include <ocilib.hpp>
Inherits ocilib::HandleHolder< OCI_Transaction *>.
Public Types | |
| enum | TransactionFlagsValues { , New = OCI_TRS_NEW, Tight = OCI_TRS_TIGHT, Loose = OCI_TRS_LOOSE, ReadOnly = OCI_TRS_READONLY, ReadWrite = OCI_TRS_READWRITE, Serializable = OCI_TRS_SERIALIZABLE } |
| Transaction flags enumerated values. More... | |
| typedef Flags< TransactionFlagsValues > | TransactionFlags |
| Transaction flags. More... | |
Public Member Functions | |
| Transaction (const Connection &connection, unsigned int timeout, TransactionFlags flags, OCI_XID *pxid=nullptr) | |
| Create a new global transaction or a serializable/read-only local transaction. More... | |
| void | Prepare () |
| Prepare a global transaction validation. | |
| void | Start () |
| Start global transaction. | |
| void | Stop () |
| Stop current global transaction. | |
| void | Resume () |
| Resume a stopped global transaction. | |
| void | Forget () |
| Cancel the prepared global transaction validation. | |
| TransactionFlags | GetFlags () const |
| Return the transaction mode. More... | |
| unsigned int | GetTimeout () const |
| Return the transaction Timeout. | |
Oracle Transaction object.
This class wraps the OCILIB object handle OCI_Transaction and its related methods
Definition at line 2441 of file ocilib.hpp.
Transaction flags.
Possible values are Transaction::TransactionFlagsValues
Definition at line 2476 of file ocilib.hpp.
Transaction flags enumerated values.
Definition at line 2452 of file ocilib.hpp.
|
inline |
Create a new global transaction or a serializable/read-only local transaction.
| connection | - Connection |
| timeout | - Time that a transaction stays inactive after being stopped |
| flags | - Transaction flags |
| pxid | - pointer to a global transaction identifier structure |
Definition at line 1658 of file ocilib_impl.hpp.
References ocilib::Check(), OCI_TransactionCreate(), and OCI_TransactionFree().
|
inline |
Return the transaction mode.
Definition at line 1693 of file ocilib_impl.hpp.
References ocilib::Check(), and OCI_TransactionGetMode().


Shettle, Patricia Mary
| Birth Name | Shettle, Patricia Mary |
| Gender | female |
| Age at Death | 82 years, 10 months, 21 days |
Narrative
SMH - REED, Patricia Mary (nee Shettle) Nov 22, 1998. At Hospital, late of East Lakes. Dearly loved wife of Frank (dec), much loved mother & mother-in-law of John & Diane, David & Mark & nanna of Justine, Katherine, Timothy & Carolyn. Aged 82 years. May she rest in peace.
A Requiem Mass for the repose of Pattie"s soul will be concelebrated at St Teresa's Catholic Church, Sutherland St, Mascot next Thursday (26 Nov, 1998), appointed to commence at 1.30 pm.
At the conclusion of the prayers following Mass, the cortege will proceed to the family grave at Waverley Cemetery.
Four Grandchildren - Justine, Katherine, Timothy & Carolyn.
Events
| Event | Date | Place | Description | Sources |
|---|---|---|---|---|
| Birth | 1916 | Waverley NSW #5253 | ||
|
|
||||
| Death | 1998-11-22 | Late of Eastlakes NSW | ||
|
|
||||
| Burial | 1998-11-26 | Waverley Cemetery NSW | ||
|
|
Parents
| Relation to main person | Name | Birth date | Death date | Relation within this family (if not by birth) |
|---|---|---|---|---|
| Father | Shettle, William John “Jack” | 1888 | 1953-07-10 | |
| Mother | Cahill, Mary Josephine | 1884 | 1919-11-29 | |
| Shettle, Patricia Mary | 1916 | 1998-11-22 | ||
| Sister | Shettle, Phyllis Joan | 1917 | 1979-03-13 | |
| Sister | Shettle, Lenore Margaret | about 1919 | 1919-11-29 |
Families
Family of Reed, Frank Arthur and Shettle, Patricia Mary |
||||||||||||||||
| Married | Husband | Reed, Frank Arthur ( * about 1915 + 1979-10-21 ) | ||||||||||||||
|
||||||||||||||||
| Children |
| Name | Birth Date | Death Date |
|---|---|---|
| Reed, [Living] | ||
| Reed, [Living] | ||
| Reed, [Living] |
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | 67DC429384C4D9119522B5F58FD686750BF2 |
Attributes
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | 62DC429384C4D9119522B5F58FD6867506A2 |


We launched the React Facebook Page along with the React v0.4 launch. 700 people already liked it to get updated on the project :)
Sophie Alpert from Khan Academy worked on a cross-browser implementation of onChange
event that landed in v0.4. She wrote a blog post explaining the various browser quirks she had to deal with.
First off, what is the input event? If you have an
<input>
element and want to receive events whenever the value changes, the most obvious thing to do is to listen to the change event. Unfortunately, change fires only after the text field is defocused, rather than on each keystroke. The next obvious choice is the keyup event, which is triggered whenever a key is released. Unfortunately, keyup doesn't catch input that doesn't involve the keyboard (e.g., pasting from the clipboard using the mouse) and only fires once if a key is held down, rather than once per inserted character.Both keydown and keypress do fire repeatedly when a key is held down, but both fire immediately before the value changes, so to read the new value you have to defer the handler to the next event loop using
setTimeout(fn, 0)
or similar, which slows down your app. Of course, like keyup, neither keydown nor keypress fires for non-keyboard input events, and all three can fire in cases where the value doesn't change at all (such as when pressing the arrow keys).
Learning a new library is always easier when you have working examples you can play with. jwh put many of them on his react-samples GitHub repo.
Some simple examples with Facebook's React framework
Cheng Lou wrote a wrapper for the Chosen input library called react-chosen. It took just 25 lines to be able to use jQuery component as a React one.
React.renderComponent(
<Chosen noResultsText="No result" value="Harvest" onChange={doSomething}>
<option value="Facebook">Facebook</option>
<option value="Harvest">Harvest</option>
</Chosen>
, document.getElementById('example'));
Domenic Denicola wrote a slide deck about the great applications of ES6 features and one slide shows how we could use Template Strings to compile JSX at run-time without the need for a pre-processing phase.
Tom Occhino and Jordan Walke, React developers, did a presentation of React at Facebook Seattle's office. Check out the first 25 minutes for the presentation and the remaining 45 for a Q&A. I highly recommend you watching this video.
Pete Hunt rewrote the entirety of the docs for v0.4. The goal was to add more explanation about why we built React and what the best practices are.
Guides


hot-shots-posix
A StatsD client forked from https://github.com/brightcove/hot-shots with additional support for POSIX-compliant systems.
Quick Start
const StatsD = require('hot-shots-posix');
// Create the client
const client = new StatsD({
path: '/path/to/uds',
protocol: 'unix_dgram',
errorHandler: (err) => {
console.error(err);
}
});
// Start emitting metrics.
client.incremment('my.stat');
client.decrement('my.stat');
Requirements
- Node.js >= 6.0.0
Development
To bootstrap your development environment:
- Clone hot-shots-posix
- Run
npm install
Tests
npm run test
- runs all testsnpm run test-unit
- runs unit testsnpm run test-integration
- runs integration testsnpm run lint
- runs linter
License
hot-shots-posix is licensed under the MIT license.


Hi there! Your browser does not allow JavaScript to run on this site. You may have a degraded experience navigating around our website. You can view the contents of any script on this site at our GitHub,
https://github.com/PurdueIEEE/IEEE-Website
.
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Find PART on Social Media:


After you have completed creating the Game of Tic Tac Toe program, put it aside.
=======
After you complete any program, put it aside.
>>>>>>> 7f1248c5f129b16822dc621de4632a2844294c2d
Recreate the solution from scratch, without looking at the solution.
When you get stuck, reflect on why. Determine what stopped you from completing it. Study related material (and yes, you can look at the solution to help you figure it out at this point).
Repeat steps 1, 2, and 3, until you can produce the solution without any help.
<<<<<<< HEAD
=======
>>>>>>> 7f1248c5f129b16822dc621de4632a2844294c2d
Study the assigned material and make notes. Complete Worksheet 4. — Post your questions on the Discussion board or bring to next class. Answer other students' questions. Start a conversation.
=======
Prepare for next class
Study the assigned material and make notes. Complete Worksheet 4. — Post your questions on the Discussion board or bring to next class. Answer other students' questions. Start a conversation.
>>>>>>> 7f1248c5f129b16822dc621de4632a2844294c2d
—


Evolve Shirt
Creeper Sweater
COD AW shirt
Ocelot Sweater
Portal Test Subject Pants
Portal Turret Skins Shirt


home
album
home
here's a memory box. a dynamic gallary for you
follow the navigation bar above or click
here.


# Load pickled data
import pickle
# Training data are in traffic-signs-data folder
training_file = "traffic-signs-data/train.p"
validation_file= "traffic-signs-data/valid.p"
testing_file = "traffic-signs-data/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']
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 IMAGES### Replace each question mark with the appropriate value.
### Use python, pandas or numpy methods rather than hard coding the results
n_train = len(X_train)
n_validation = len(X_valid)
n_test = len(X_test)
image_shape = X_train[0].shape
n_classes = len(set(y_train))
print("Number of training examples =", n_train)
print("Number of validation examples =", n_validation)
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.
# My helper functions for visualize and debug
import csv
# Load sign name database and create a map that we can use later
id_to_sign = {} # map for storing id to sign name mapping
with open('signnames.csv', 'r') as csvfile:
reader = csv.reader(csvfile, delimiter=',')
for row in reader:
id_to_sign[row[0]] = row[1]
# Output single image
def visual(X):
image = X.squeeze()
plt.figure(figsize=(2,2))
plt.imshow(image,cmap="gray")
# Output traffic sign with a string label
def visual_with_label(X,Y):
image = X.squeeze()
plt.figure(figsize=(2,2))
plt.imshow(image,cmap="gray")
plt.figtext(0,1,id_to_sign[str(Y)])
def visual_with_top_k(X,y):
image = X.squeeze()
plt.figure(figsize=(2,2))
plt.imshow(image,cmap="gray")
buf = ""
for i in range(len(y.indices[0])):
buf += "%s %f%%\n" % (
id_to_sign[str(y.indices[0][i])],
y.values[0][i])
plt.figtext(0,1,buf)
# Visualize 20 picked data in the set
def visual_20(X,Y):
data = range(250,30000,1500)
for index in data:
image = X[index].squeeze()
#print(image)
#print(np.mean(image))
plt.figure(figsize=(2,2))
plt.imshow(image,cmap="gray")
plt.figtext(0.5,0,id_to_sign[str(Y[index])],ha="center")
#filename = "sample/%d.png" % i
#plt.savefig(filename)
# Show some statistics
from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
# display distribution of given dataset
def plt_distribution(data, title):
counter = Counter(data) # output a map of label:count
counts = []
labels = []
for i in range( len(set(data)) ):
counts.append(counter[i])
labels.append(id_to_sign[str(i)])
fig, ax = plt.subplots( figsize=(15,5) )
ind = np.arange(len(set(data)))
width = 0.8
rects1 = ax.bar(ind, counts, width,
color='SkyBlue', label='Count')
ax.set_xticks(ind)
ax.set_xticklabels(labels, rotation="vertical")
ax.legend()
ax.set_title(title, fontdict={"fontsize":20})
plt.show()
plt_distribution(y_train, "Training set")
plt_distribution(y_valid, "Validation set")
plt_distribution(y_test, "Test set")
### Data exploration visualization code goes here.
### Feel free to use as many code cells as needed.
import matplotlib.pyplot as plt
import random
# Visualizations will be shown in the notebook.
%matplotlib inline
visual_20(X_train,y_train)
from sklearn.utils import shuffle
import numpy as np
from skimage import exposure
#X_train, y_train = shuffle(X_train, y_train)
def preprocess_img(rgb):
# Convert to grayscale
# https://stackoverflow.com/questions/12201577/how-can-i-convert-an-rgb-image-into-grayscale-in-python
new = np.ndarray(shape=(32,32,1),dtype=np.float32)
mode = 1
if mode==1:
# equalize exposure after grayscale
r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]
# we use grayscale anyway.
# colour doesn't outperforms grayscale one
y = (0.2989 * r + 0.5870 * g + 0.1140 * b).astype(np.float32)
#u = (0.492*(b-y)).astype(np.float32)
#v = (0.877*(r-y)).astype(np.float32)
#new[:,:,0] = (y/255.0)*2.0-1.0
new[:,:,0] = (exposure.equalize_adapthist(y/255.0))*2.0-1.0
elif mode == 2:
# equalize exposure before grayscale
rgb = exposure.equalize_adapthist(rgb)
r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]
new[:,:,0] = ((0.2989 * r + 0.5870 * g + 0.1140 * b)/255.0).astype(np.float32)
elif mode == 3:
# graysacle without green channel
r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]
new[:,:,0] = ((0.2989 * r + 0 * g + 0.1140 * b)/255.0).astype(np.float32)
elif mode == 4:
r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]
tmp = ((0.2989 * r + 0 * g + 0.1140 * b)/255.0).astype(np.float32)
new[:,:,0] = exposure.equalize_adapthist(tmp)
else:
r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]
y = (0.2989 * r + 0.5870 * g + 0.1140 * b).astype(np.float32)
new[:,:,0] = ( (y/255.0)*2.0-1.0) .astype(np.float32)
return new
def preprocess(X):
X_new = []
for i in range(len(X)):
X_new.append( preprocess_img(X[i]) )
return X_new
### 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.
# Use cached processed image, which saves time when tuning the network
CACHE = False
BUILD_CACHE = False
training_cache = "traffic-signs-data.processed/X_train.p"
validation_cache = "traffic-signs-data.processed/X_valid.p"
testing_cache = "traffic-signs-data.processed/X_test.p"
if CACHE:
with open(training_cache, mode='rb') as f:
X_train = pickle.load(f)
with open(validation_cache, mode='rb') as f:
X_valid = pickle.load(f)
with open(testing_cache, mode='rb') as f:
X_test = pickle.load(f)
else:
# preprocess all the data
X_train = preprocess(X_train)
X_valid = preprocess(X_valid)
X_test = preprocess(X_test)
if BUILD_CACHE:
with open(training_cache, mode='wb') as f:
pickle.dump(X_train,f)
with open(validation_cache, mode='wb') as f:
pickle.dump(X_valid,f)
with open(testing_cache, mode='wb') as f:
pickle.dump(X_test,f)
visual_20(X_train,y_train)
import tensorflow as tf
# Training parameters
EPOCHS = 20
BATCH_SIZE = 64
# Learn rate here
LEARN_RATE = 0.001
from tensorflow.contrib.layers import flatten
# Exp 1
# Vanilla LeNet for digits recognition and just modify output class from 10 to n_classes
def LeNet(x):
# Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer
mu = 0
sigma = 0.1
# SOLUTION: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.
conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma))
conv1_b = tf.Variable(tf.zeros(6))
conv1 = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b
# SOLUTION: Activation.
conv1 = tf.nn.relu(conv1)
# SOLUTION: Pooling. Input = 28x28x6. Output = 14x14x6.
conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
# SOLUTION: Layer 2: Convolutional. Output = 10x10x16.
conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))
conv2_b = tf.Variable(tf.zeros(16))
conv2 = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b
# SOLUTION: Activation.
conv2 = tf.nn.relu(conv2)
# SOLUTION: Pooling. Input = 10x10x16. Output = 5x5x16.
conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
# SOLUTION: Flatten. Input = 5x5x16. Output = 400.
fc0 = flatten(conv2)
# SOLUTION: Layer 3: Fully Connected. Input = 400. Output = 120.
fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))
fc1_b = tf.Variable(tf.zeros(120))
fc1 = tf.matmul(fc0, fc1_W) + fc1_b
# SOLUTION: Activation.
fc1 = tf.nn.relu(fc1)
# SOLUTION: Layer 4: Fully Connected. Input = 120. Output = 84.
fc2_W = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))
fc2_b = tf.Variable(tf.zeros(84))
fc2 = tf.matmul(fc1, fc2_W) + fc2_b
# SOLUTION: Activation.
fc2 = tf.nn.relu(fc2)
# SOLUTION: Layer 5: Fully Connected. Input = 84. Output = n_classes
# change output class from 10 to n_classes
fc3_W = tf.Variable(tf.truncated_normal(shape=(84, n_classes), mean = mu, stddev = sigma))
fc3_b = tf.Variable(tf.zeros(n_classes))
logits = tf.matmul(fc2, fc3_W) + fc3_b
return logits
# Exp 2
# Put both Conv1 and Conv2 into fully connected layer and apply dropout
def LeNet2(x):
# Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer
mu = 0
sigma = 0.1
# SOLUTION: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.
conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma))
conv1_b = tf.Variable(tf.zeros(6))
conv1 = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b
# SOLUTION: Activation.
conv1 = tf.nn.relu(conv1)
# SOLUTION: Pooling. Input = 28x28x6. Output = 14x14x6.
conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
# SOLUTION: Layer 2: Convolutional. Output = 10x10x16.
conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))
conv2_b = tf.Variable(tf.zeros(16))
conv2 = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b
# SOLUTION: Activation.
conv2 = tf.nn.relu(conv2)
# SOLUTION: Pooling. Input = 10x10x16. Output = 5x5x16.
conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
# SOLUTION: Flatten. Input = 14x16x6+5x5x16. Output = 1576.
#fcA = tf.concat(concat_dim=1,values=[tf.nn.dropout(flatten(conv1), keep_rate), flatten(conv2)])
fcA = tf.concat(concat_dim=1,values=[
tf.nn.dropout(flatten(conv1), keep_rate),
tf.nn.dropout(flatten(conv2), keep_rate)])
# Extra Fully Connected Layer: Input: 1576. Output = 400
fc0_W = tf.Variable(tf.truncated_normal(shape=(1576, 400), mean = mu, stddev = sigma))
fc0_b = tf.Variable(tf.zeros(400))
fc0 = tf.matmul(fcA, fc0_W) + fc0_b
# SOLUTION: Layer 3: Fully Connected. Input = 400. Output = 120.
fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))
fc1_b = tf.Variable(tf.zeros(120))
fc1 = tf.matmul(fc0, fc1_W) + fc1_b
# SOLUTION: Activation.
fc1 = tf.nn.relu(fc1)
# SOLUTION: Layer 4: Fully Connected. Input = 120. Output = 84.
fc2_W = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))
fc2_b = tf.Variable(tf.zeros(84))
fc2 = tf.matmul(fc1, fc2_W) + fc2_b
# SOLUTION: Activation.
fc2 = tf.nn.relu(fc2)
# SOLUTION: Layer 5: Fully Connected. Input = 84. Output = n_classes
# change output class from 10 to n_classes
fc3_W = tf.Variable(tf.truncated_normal(shape=(84, n_classes), mean = mu, stddev = sigma))
fc3_b = tf.Variable(tf.zeros(n_classes))
logits = tf.matmul(fc2, fc3_W) + fc3_b
return logits
# Exp 3
# Vanilla LeNet with dropout after convolution layers
#
# visualization output
def LeNet3(x, output=""):
# Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer
mu = 0
sigma = 0.1
# SOLUTION: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.
conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma))
conv1_b = tf.Variable(tf.zeros(6))
conv1 = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b
if(output=="conv1"): # for visualization
return conv1
# SOLUTION: Activation.
conv1 = tf.nn.relu(conv1)
# SOLUTION: Pooling. Input = 28x28x6. Output = 14x14x6.
conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
# SOLUTION: Layer 2: Convolutional. Output = 10x10x16.
conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))
conv2_b = tf.Variable(tf.zeros(16))
conv2 = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b
if(output=="conv2"): # for visualization
return conv2
# SOLUTION: Activation.
conv2 = tf.nn.relu(conv2)
# SOLUTION: Pooling. Input = 10x10x16. Output = 5x5x16.
conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
# SOLUTION: Flatten. Input = 5x5x16. Output = 400.
# Apply dropout after f
fc0 = tf.nn.dropout(flatten(conv2), keep_rate)
# SOLUTION: Layer 3: Fully Connected. Input = 400. Output = 120.
fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))
fc1_b = tf.Variable(tf.zeros(120))
fc1 = tf.matmul(fc0, fc1_W) + fc1_b
# SOLUTION: Activation.
fc1 = tf.nn.relu(fc1)
# SOLUTION: Layer 4: Fully Connected. Input = 120. Output = 84.
fc2_W = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))
fc2_b = tf.Variable(tf.zeros(84))
fc2 = tf.matmul(fc1, fc2_W) + fc2_b
# SOLUTION: Activation.
fc2 = tf.nn.relu(fc2)
# SOLUTION: Layer 5: Fully Connected. Input = 84. Output = n_classes
# change output class from 10 to n_classes
fc3_W = tf.Variable(tf.truncated_normal(shape=(84, n_classes), mean = mu, stddev = sigma))
fc3_b = tf.Variable(tf.zeros(n_classes))
logits = tf.matmul(fc2, fc3_W) + fc3_b
return logits
x = tf.placeholder(tf.float32, (None, 32, 32,1)) # input as 32x32 grayscale image
y = tf.placeholder(tf.int32, (None)) # y label
one_hot_y = tf.one_hot(y, n_classes) # labels in one hot encoding
# we don't have dropout except in training mode, which dropout rate are given via feed dict
keep_rate = tf.placeholder_with_default(1.0, (None))
logits = LeNet3(x)
out_conv1 = LeNet3(x,"conv1")
out_conv2 = LeNet3(x,"conv2")
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits)
loss_operation = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate = LEARN_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()
# get accuaracy of the model with given dataset
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})
total_accuracy += (accuracy * len(batch_x))
return total_accuracy / num_examples
# helper function to get loss
def get_loss(X_data, y_data):
sess = tf.get_default_session()
loss = sess.run(loss_operation, feed_dict={x: batch_x, y: batch_y})
return loss
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.
import matplotlib.pyplot as plt
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
num_examples = len(X_train)
print("Training...")
print()
best_accuracy = 0
epoches = []
history_train = []
history_valid = []
history_loss = []
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_rate: 0.6 }) # dropout rate set here
train_accuracy = evaluate(X_train, y_train)
validation_accuracy = evaluate(X_valid, y_valid)
loss = get_loss(X_train, y_train)
print("EPOCH {} ...".format(i+1))
if False: # Print accuracy or not
print("Train Accuracy = {:.3f}".format(train_accuracy) )
print("Validation Accuracy = {:.3f}".format(validation_accuracy) )
#test_accuracy = evaluate(X_test, y_test)
#print("Test Accuracy = {:.3f}".format( test_accuracy ))
print("loss = {:.3f}".format(loss))
print()
# for graphing
epoches.append(i+1)
history_train.append(train_accuracy)
history_valid.append(validation_accuracy)
history_loss.append(loss)
# keep best accuracy model we ever met
if validation_accuracy > best_accuracy:
best_accuracy = validation_accuracy
saver.save(sess, './lenet')
print("Best Validation Accuracy = {:.3f}".format(best_accuracy))
fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,5) )
ax1.plot(epoches,history_train,'bo-',label="train")
ax1.plot(epoches,history_valid,'go-',label="valid")
ax1.set_xticks(epoches)
ax1.set_xticklabels(epoches)
ax1.set_title('Accuracy history')
ax2.plot(epoches,history_loss,'ro-',label="loss")
ax2.set_xticks(epoches)
ax2.set_xticklabels(epoches)
ax2.set_title('Loss history')
plt.show()
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 matplotlib.image as mpimg
import os
def list_images():
filelist = os.listdir("images/")
result = []
for file in filelist:
name,ext = os.path.splitext(file)
if ext not in ['.png','.jpg']:
continue
filename = 'images/%s' % file
result.append(filename)
return result
filelist = os.listdir("images/")
for filename in list_images():
img_orig = mpimg.imread(filename,0)
visual(img_orig)
### 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.
import matplotlib.image as mpimg
import os
import re
total = 0
correct = 0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, tf.train.latest_checkpoint('.'))
for filename in list_images():
# extract label from filename. ex: label is 5 when filename is 1[5].png
label = int(re.search("\[(\d+)]",filename).group(1))
img_orig = mpimg.imread(filename,0)
img = preprocess_img(img_orig)
img = img.astype('float32')
img = img.reshape(1,32,32,1)
y=sess.run(tf.argmax(logits, 1), feed_dict={x: img})
if(y[0] == label):
correct+=1
total+=1
visual_with_label(img_orig,y[0])
### 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.
print("Accuracy for given images: %.3f%%" % ((correct/total)*100) )
### 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.
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, tf.train.latest_checkpoint('.'))
for filename in list_images():
img_orig = mpimg.imread(filename,0)
img = preprocess_img(img_orig)
img = img.astype('float32')
img = img.reshape(1,32,32,1)
y=sess.run(tf.nn.top_k(logits, 5), feed_dict={x: img})
visual_with_top_k(img_orig,y)
### Visualize your network's feature maps here.
### Feel free to use as many code cells as needed.
# image_input: the test image being fed into the network to produce the feature maps
# tf_activation: should be a tf variable name used during your training procedure that represents the calculated state of a specific weight layer
# activation_min/max: can be used to view the activation contrast in more detail, by default matplot sets min and max to the actual min and max values of the output
# plt_num: used to plot out multiple different weight feature map sets on the same block, just extend the plt number for each new feature map entry
def outputFeatureMap(image_input, tf_activation, activation_min=-1, activation_max=-1 ,plt_num=1):
# Here make sure to preprocess your image_input in a way your network expects
# with size, normalization, ect if needed
# image_input =
# Note: x should be the same name as your network's tensorflow data placeholder variable
# If you get an error tf_activation is not defined it may be having trouble accessing the variable from inside a function
activation = tf_activation.eval(session=sess,feed_dict={x : image_input})
featuremaps = activation.shape[3]
plt.figure(plt_num, figsize=(15,15))
for featuremap in range(featuremaps):
plt.subplot(6,8, featuremap+1) # sets the number of feature maps to show on each row and column
plt.title('FeatureMap ' + str(featuremap)) # displays the feature map number
if activation_min != -1 & activation_max != -1:
plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmin =activation_min, vmax=activation_max, cmap="gray")
elif activation_max != -1:
plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmax=activation_max, cmap="gray")
elif activation_min !=-1:
plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmin=activation_min, cmap="gray")
else:
plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", cmap="gray")
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, tf.train.latest_checkpoint('.'))
plt_count = 0
for filename in list_images():
img_orig = mpimg.imread(filename,0)
img = preprocess_img(img_orig)
img = img.astype('float32')
img = img.reshape(1,32,32,1)
plt_count+=1
outputFeatureMap(img,out_conv1, plt_num=plt_count)
plt_count+=1
outputFeatureMap(img,out_conv2, plt_num=plt_count)


If you want to contact me, the best way to do so is email me Here
You can also find me on GitHub and CodePen. Links are below.


Ford unveils electric Mustang … and it’s a manual!
Ford has already teased us with a Mustang-inspired electric SUV, now it has taken the covers off an electric version of its most famous muscle car.
by Joshua Dowling- shares
Is the next-generation Ford Mustang going to have a fully electric option to sell alongside its V8?
Talk about having an each way bet. While you could barely find the entrance to the 2019 SEMA auto show through clouds of thick tyre smoke from Ford Mustangs doing burnouts and drifting demonstrations in the car park, the company took a green approach inside the Las Vegas exhibition halls.
The Ford Mustang Lithium is an “ultra-high-performance battery electric Mustang fastback prototype”.
It has a claimed 670kW of power and an astonishing 1355Nm of torque (900 horsepower and 1000 foot-pound of torque in the old money) that Ford claims is “instantaneously available” due to the way electric motors deliver their acceleration.
Ford says the Mustang Lithium “amps muscle car performance to a new level and helps gauge the level of interest the next wave of performance customers have in lightning-quick performance that only fully electric powertrains can deliver”.
To tame this phenomenal power, the Ford Mustang Lithium has four drive modes that limit output depending on the scenario: Valet, Sport, Track and Beast.
Perhaps there should be another mode for “leaving a cars-and-coffee event”, to curb the endless videos on YouTube of Mustang V8s making grand departures only to end up in a fender bender.
The Mustang Lithium is not just a show car, “but a testbed for battery and thermal management technologies in ultra-high-performance electric vehicles”, says Ford.
“Ford has made no secret of the fact that we are electrifying our most popular nameplates,” said Hau Thai-Tang, Ford's chief product development and purchasing officer in a media statement.
“This one-off Mustang prototype is a great opportunity … to showcase to our customers what new electrified powertrains can do for performance in a car they already know and love.”
The “street-ready” Mustang Lithium has custom carbon fibre body panels, a 1.0-inch lowered stance and 20-inch forged alloy wheels (wider at the rear for extra grip).
Under the bonnet the V8 has been replaced by a dual-core electric motor and dual power inverters, powered by an 800-volt Webasto battery system with EVDrive Technology that can discharge a mega-watt of electrical energy.
With 800 volts the Mustang Lithium has twice the voltage of most electric cars on the road today. Ford says this allows the system to be “lighter, more powerful and generate less heat, and more electric force than most battery-electric systems”.
Unlike every other electric car on sale today, the Ford Mustang Lithium has a manual transmission (for the rev heads it’s a drag-strip-ready Calimer-version of the Getrag MT82 six-speed manual with billet internals to handle the epic torque).
Power is sent to the rear wheels via a heavy duty differential and Ford Performance half shafts.
Additional features include the V8 Mustang’s optional Track Handling Pack and strut tower brace, and race-ready six-piston front brakes from the Shelby GT350R.
A statement from battery supplier Webasto said: “Very similar to Ford’s push for vehicle electrification, this is an innovative way to further the technologies we're developing to enable vehicle electrification while creating a vision of how cool the future of electric performance will be”.
Now, about the sound of electric cars…
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Ford unveils electric Mustang … and it’s a manual!


| Trees | Indices | Help |
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PDF handling for eLink including Printall functionality
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Server Server for PDF generation from Unform |
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HtmlConversionRequest Request class for handling the Request to the HtmlToPDF server. |
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HtmlConverter Convert Html pages to PDF by requesting from server. |
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HtmlPDFRetriever Retrieves static pdf files from the HtmlPDF server |
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UnformStringRetriever String suitable for Unform |
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UploadDoc Upload merged document |
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PDFStringInvalidError Returned when PDF string for Unform is invalid |
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Log a message to pdfserver.log
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Read a pdf file
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Write out a pdf file
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Merge all pages of all files
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Merge PdfFileReader objects
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Retrieve pdf from storage directory
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Server pdf to the browser
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Create a string representing the filename
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| Trees | Indices | Help |
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| Generated by Epydoc 3.0.1 on Mon Oct 8 11:15:35 2012 | http://epydoc.sourceforge.net |


{% set current_link = "home" %} {% extends "labmanager_admin/layout-logged.html" %} {% block title %} Index {% endblock %} {% block body %} {{ super() }}
Welcome to the lab manager!
A Lab Manager supports multiple Learning Management Systems (LMSs). From this Lab Manager you, as an administrator, can add, remove and modify the existing LMSs.
A Lab Manager supports multiple Remote Laboratory Management Systems (RLMSs). From this Lab Manager you can add, remove them, and assign them to LMSs.


A+B for Input-Output Practice (I)
问题描述 :
Your task is to Calculate a + b.
Too easy?! Of course! I specially designed the problem for acm beginners.
You must have found that some problems have the same titles with this one, yes, all these problems were designed for the same aim.
Too easy?! Of course! I specially designed the problem for acm beginners.
You must have found that some problems have the same titles with this one, yes, all these problems were designed for the same aim.
输入:
The input will consist of a series of pairs of integers a and b, separated by a space, one pair of integers per line.
输出:
For each pair of input integers a and b you should output the sum of a and b in one line, and with one line of output for each line in input.
样例输入:
1 5 10 20
样例输出:
6 30
2011-12-12 04:39:43
地址：http://acm.hdu.edu.cn/showproblem.php?pid=1089
题意：同1000
代码（C编译）：
main(a,b){while(~scanf("%d%d",&a,&b))printf("%d\n",a+b);}
(adsbygoogle = window.adsbygoogle || []).push({});
怎么也成不了精明的人
很好的题，还是能做出几道的，嘿嘿
第二个方法很不错啊
使用ListNodeWrapper很蹩脚的感觉，没有更好的解决办法？
mark, 备用
有些说不通吧，只有1个孩子肯定是男孩了
加了星号 (*) 表示跳过此数据不读入. 因为scanf是不读取本行回车的
约瑟夫也用说这么长……很成熟的一个问题了，分治的方法解起来o(n)就可以了，有兴趣可以看看具体数学的第一章，关于约瑟夫问题推导出了一系列的结论，很漂亮
原来欧几里德定理应用这么多
上面的评论很起劲，为什么我看不懂啊
有的，已更新
27行 minStack.push(minValue); 去掉就对了
不考虑最后将结果排序的话，快排的时间复杂度是O(N) ，而堆排的是O(N*logK)
看来学好代数很重要啊


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| Gene Name | Patient Count | Patient IDs |
|---|---|---|
| {{diseaseFiltered.geneName}} | {{diseaseFiltered.patientIDs.length}} | {{patient.targetID}} |
| Disease Type | Gene Name | Patient ID | Result | VUS |
|---|---|---|---|---|
| {{patient.conditionSubtype}}{{patient.conditionName}} | {{patient.geneName}} | {{patient.targetID}} |
The search will return alterations with status of 'known' or 'likely', and any alterations with no assigned status. If VUS are included in the search then those with status of 'unknown' and 'ambiguous' are also returned.
| Patient Count | Type | Patient IDs |
|---|---|---|
| {{($parent.search.geneList.patientIDs|filter:types.type).length}} | {{types.type}} | {{patient.targetID}} |
| Disease Type | Patient ID | Variant in Selected Gene | VUS |
|---|---|---|---|
| {{patient.conditionSubtype}}{{patient.conditionName}} | {{patient.targetID}} |
The search will return alterations with status of 'known' or 'likely', and any alterations with no assigned status. If VUS are included in the search then those with status of 'unknown' and 'ambiguous' are also returned.
| Patient ID | CDX/PDX |
|---|---|
| None | |
| {{pdxcdx.targetID}} | {{type.pdxcdx}} |


Customs Comptroller-General to sack officers living above earnings
by NANCol. Hameed Ali (rtd), the Comptroller-General of Nigeria Customs Service (NCS) says he will sack any officer who cannot live within his or her remuneration and involve in illicit ways to acquire wealth.
Ali gave the warning while decorating some newly promoted officers of the service in Abuja on Friday.
News Agency of Nigeria (NAN) reports that the service recently promoted 2,508 officers to various ranks and appointed three as members of its management.
The customs boss warned that no officer in the service should live above his or her earnings if they desire to remain in service.
According to him, every officer could survive within the remuneration package of the service, if well managed.
“I will show the way out to any officer who thinks he can not survive with his remuneration but involve in illicit ways to acquire wealth.
“I know the service gives you enough that you can survive with, it is just a question of prudence.

Ali charged the newly promoted officers to be more committed and dedicated to their duties.
He advised them to take their jobs seriously, be fair and firm in ther decisions and actions.
Ali enjoined the promoted officers to uphold the change mantra in the service and be part of the reform being carried out.
Mr David Chikan, Deputy Comptroller-General who spoke on behalf of the promoted officers thanked the management for their elevations.
Chikan assured that they would justify the confidence reposed in them.


--- layout: post title: CNC3018 Woodpecker Grbl Firmware Notes date: '2020-10-12T11:17:00.001-07:00' author: gojimmypi tags: - G-Code. - Woodpecker - CNC - GRBL modified_time: '2020-10-12T12:22:03.964-07:00' thumbnail: https://1.bp.blogspot.com/-dQc392uFyt8/X4NUowUrYWI/AAAAAAAACzE/vZi1cRVURdoGxx2N9a1GIoIZHL7QlSDigCLcBGAsYHQ/s72-c/putty_8N1.png blogger_id: tag:blogger.com,1999:blog-4109066286647243251.post-3358566534064741943 blogger_orig_url: https://gojimmypi.blogspot.com/2020/10/cnc3018-woodpecker-grbl-firmware-notes.html ---
The Woodpecker boards shares the USB 5VDC line with the power input. I use a non-powered USB hub between my computer and the CNC machine. Honestly I'm not sure if it actually does anything to isolate or perhaps I'm just lucky. (probably the latter) TODO: find and buy a good isolated USB, such as the Adafruit USB Isolator - 100mA Isolated Low/Full Speed USB.
For reference, my current firmware version is Grbl 1.1f, or more specifically $I
result:
[VER:1.1f.20170801:]
[OPT:V,15,128]
"Help" $$
is simply this one line:
[HLP:$$ $# $G $I $N $x=val $Nx=line $J=line $SLP $C $X $H ~ ! ? ctrl-x]
$$ (view Grbl settings)
$# (view # parameters)
$G (view parser state)
$I (view build info)
$N (view startup blocks)
$x=value (save Grbl setting)
$Nx=line (save startup block)
$C (check gcode mode)
$X (kill alarm lock)
$H (run homing cycle)
~ (cycle start)
! (feed hold)
? (current status)
ctrl-x (reset Grbl)
My settings via $$
(always a good idea to save these BEFORE upgrading firmware):
$0=10
$1=25
$2=0
$3=5
$4=0
$5=0
$6=0
$10=1
$11=0.010
$12=0.002
$13=0
$20=0
$21=0
$22=0
$23=0
$24=25.000
$25=500.000
$26=250
$27=1.000
$30=1000
$31=0
$32=0
$100=800.000
$101=800.000
$102=800.000
$110=800.000
$111=800.000
$112=600.000
$120=10.000
$121=10.000
$122=10.000
$130=200.000
$131=200.000
$132=200.000
The Grbl Default Settings:
{% include code_header.html %}
$0=10 (step pulse, usec)
$1=25 (step idle delay, msec)
$2=0 (step port invert mask:00000000)
$3=6 (dir port invert mask:00000110)
$4=0 (step enable invert, bool)
$5=0 (limit pins invert, bool)
$6=0 (probe pin invert, bool)
$10=3 (status report mask:00000011)
$11=0.020 (junction deviation, mm)
$12=0.002 (arc tolerance, mm)
$13=0 (report inches, bool)
$20=0 (soft limits, bool)
$21=0 (hard limits, bool)
$22=0 (homing cycle, bool)
$23=1 (homing dir invert mask:00000001)
$24=50.000 (homing feed, mm/min)
$25=635.000 (homing seek, mm/min)
$26=250 (homing debounce, msec)
$27=1.000 (homing pull-off, mm)
$100=314.961 (x, step/mm)
$101=314.961 (y, step/mm)
$102=314.961 (z, step/mm)
$110=635.000 (x max rate, mm/min)
$111=635.000 (y max rate, mm/min)
$112=635.000 (z max rate, mm/min)
$120=50.000 (x accel, mm/sec^2)
$121=50.000 (y accel, mm/sec^2)
$122=50.000 (z accel, mm/sec^2)
$130=225.000 (x max travel, mm)
$131=125.000 (y max travel, mm)
$132=170.000 (z max travel, mm)
$#
result:
[G54:0.000,0.000,0.000]
[G55:0.000,0.000,0.000]
[G56:0.000,0.000,0.000]
[G57:0.000,0.000,0.000]
[G58:0.000,0.000,0.000]
[G59:0.000,0.000,0.000]
[G28:0.000,0.000,0.000]
[G30:0.000,0.000,0.000]
[G92:0.000,0.000,0.000]
[TLO:0.000]
[PRB:0.000,0.000,0.000:0]
Whistling sound? Thanks chamnit:
The PWM frequency changed from 7.8 kHz to 1kHz in Grbl v1.1. To change back, edit the cpu_map.h file and look for the SPINDLE_TCCRB_INIT_MASK macros. There are several commented, including the one for 1kHz. Just swap them and recompile
If for some reason I ever need to replace the controller board, I'll probably try the SAINSMART Controller Board for Genmtisu CNC Router 3018, 3018-PRO, 1810-RP and the Offline Controller Module (although need to determine if there's anything hardware specific to their Genmtisu)
Resources, Inspiration, Credits, and Other Links:


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If I was at work today: http://www.toothpastefordinner.com/index.php?date=092111
9/22/2011, 12:23:46 PM
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link--- layout: tweet ---
If I was at work today: http://www.toothpastefordinner.com/index.php?date=092111
9/22/2011, 12:23:46 PM
Favs: 0
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link


Police re-appeal to trace "despicable" thug who robbed Paisley pensioner
Officers are still searching for the man who entered the 88-year-old woman's home and stole a three figure sum of cash.
by David CampbellPolice have re-appealed to members of the community to help trace a “despicable” thug who robbed a Paisley pensioner in her own home.
Around 6.25pm on Thursday, November 14, a man entered a house in Windsor Crescent and assaulted the 88 year-old woman.
He then stole a three figure sum of cash, her bank cards and house keys.
Although she did not require hospital treatment, she has been left badly shaken as a result of this incident.
The man responsible is described as aged between 30 and 40 years and around 5ft 4 ins in height and of medium build with short cropped red hair.
He was w
earing a brown jacket.
Detective Inspector Robert Bowie of Paisley Police Station said: “Although officers have carried out a number of enquiries in the area, we have not yet traced the man responsible for this despicable crime, and I am again asking the public to think back in case they remember something that will assist our enquiries.
“My officers are continuing to study CCTV footage from in and around Windsor Crescent. I would like to hear from anyone who witnessed anyone acting suspiciously in the area, or who recognises the description of the suspect as a matter of urgency.”
Anyone with information should contact Paisley Police Station through 101 quoting reference number 3048 of Thursday 14 November 2019.
Alternatively please contact Crimestoppers can be contacted on 0800 555 111, where anonymity can be maintained.


These dAPPs come from the community. The information on this page is provided by the project owner. NEO Council and ndapp.org does not endorse these projects.
NEX combines the NEO blockchain with an off-chain matching engine to enable much faster and more complex trades than existing decentralized exchanges
Moonlight is a distributed workforce and analytical project management platform featuring a global public ledger of contributor work experience and a new match-making algorithm to effectively fulfill project needs.
AdEx is a decentralized ad exchange built on the blockchain and smart contracts. The core feature of AdEx will be the so-called AdEx User Profile - a personalized page that allows every end user to understand and control the ads delivered to them.
Qlink, developed by Qlink Foundation in Singapore, adopts the blockchain technology and creates a decentralized mobile network for P2Peer WiFi sharing, mobile data converted content distribution, enterprise telecom services and crowd-sourcing base stations (including WiFi hotspots ) set-up, generally in much lower cost and ultimate secured format.
Peer-to-peer courier platform. Couriers transport items to fulfill demands and earn courier fees.
BlockAuth allows users to login (authenticate) to apps and websites by invoking a NEO smart contract from a NEO address tied to the user’s account. It aims to be the Blockchain OpenID.
Phantasma is a platform where the users control their own content, instead of relying in third parties servers. The platform support any kind of transactions between users, eg: email, chat, files, money transfers. NEO was used for this project due to its fast transactions and C# support, combined with IPFS (a distributed file system) to store the actual messages, encrypted with the keys from the NEO wallets.
KRYPTON is a Smart Contract and ecosystem to provide SIP registration on the NEO blockchain using a KRYPTON compatible SIP provider.
Lucky NEO allows anyone to send their extra gas to a raffle contest. One winner will be chosen every two weeks and automatically paid out. Lucky NEO uses an admin account to automatically pay out the winner to the address that sent the funds; so you don’t have to follow the contest and claim the gas yourself.
Protocol and smart contract for trading NEP-5 based assets.NepSwap is a protocol and smart contract for trading NEP-5 assets. This prototype idea is based on Etherdelta and Bancor. It doesn’t have a personal token or ICO, this project is intended for community, and non-profit. NepSwap is generic, so any asset at any time can be traded or kept as balance on the account.
Smart Promise is an electronic journal of smart promises developed through Blockchain. User of such an environment receives a reward for each of his fulfilled promises. The concept of the idea contains the possibility to get a strong motivation to action that user pointed in promise.
This dApp allows you to insure against bad weather conditions on a given day. If the relative sunshine duration on that day is lower than 50 percent, you get paid.
Distributed domain name service ending with .neo based on NEO blockchain
THEKEY is a Decentralized Ecosystem of Identity Verification Tool Using National Big-data and Blockchain.
THEKEY Project Team is now developing an identification verification (IDV) tool with blockchain based dynamic multi-dimension identification (BDMI) by using Personally Identifiable Information (PII) which is exclusively authorized by government authorities.
Trinity is applicable to blockchain transfer of NEP-5 standard tokens.
As a Neo-version lightening network, Trinity achieves real-time payment, low transaction fees, scalability, and privacy protection of Neo mainnet assets through state channels technology.
GAME.FUND is a public community for make games, play games, and digital assets trade, which of the people, by the people, for the people based on blockchain. "BCreator.io" is our own game making tool(Close alpha ready).
Concierge is a Travel Booking Marketplace with 0% commission fees using the NEO Blockchain. Vendors and consumers can connect directly. We will accept fiat and crypto as payment for bookings. CGE is the utility token for the platform.
A pet game based on NEO blockchain, and a salute to Cryptokitties. 2018 is Asian lunar year of dog. Players can buy, breed and sell digital dogs which stored in NEO block chain. Every dog is a unique and traded non changeable object in block chain.
AISC is a graphical smart contract creation tool, automatically generate contract source code. Get rid of the shackles of smart contracts only can be written by programmers, back to the convenience of traditional text contract writing.
Orbis is a mobile mesh networking platform built on the NEO Blockchain. Our SDK will be publicly available for mesh app development while we will natively offer offline blockchain transaction support and IoT implementations.
Aphelion (APH) is revolutionary peer-to-peer decentralized trading platform. The core feature is a Distributed Exchange Asset Ledger (DEAL) that fuels the smart contract Aphelion DApp built on the NEO blockchain.


| predict.coxph {survival} | R Documentation |
Compute fitted values and regression terms for a model fitted by
coxph
## S3 method for class 'coxph' predict(object, newdata, type=c("lp", "risk", "expected", "terms", "survival"), se.fit=FALSE, na.action=na.pass, terms=names(object$assign), collapse, reference=c("strata", "sample"), ...)
object |
the results of a coxph fit. |
newdata |
Optional new data at which to do predictions. If absent predictions are for the data frame used in the original fit. When coxph has been called with a formula argument created in another context, i.e., coxph has been called within another function and the formula was passed as an argument to that function, there can be problems finding the data set. See the note below. |
type |
the type of predicted value.
Choices are the linear predictor ( |
se.fit |
if TRUE, pointwise standard errors are produced for the predictions. |
na.action |
applies only when the |
terms |
if type="terms", this argument can be used to specify which terms should be included; the default is all. |
collapse |
optional vector of subject identifiers. If specified, the output will contain one entry per subject rather than one entry per observation. |
reference |
reference for centering predictions, see details below |
... |
For future methods |
The Cox model is a relative risk model; predictions
of type "linear predictor", "risk", and "terms" are all
relative to the sample from which they came. By default, the reference
value for each of these is the mean covariate within strata. The
primary underlying
reason is statistical: a Cox model only predicts relative risks
between pairs of subjects within the same strata, and hence the addition
of a constant to any covariate, either overall or only within a
particular stratum, has no effect on the fitted results.
Using the reference="strata"
option causes this to be true for
predictions as well.
When the results of predict
are used in further calculations it
may be desirable to use a fixed reference level.
Use of reference="sample"
will use the overall means, and agrees
with the linear.predictors
component of the coxph object (which
uses the overall mean for backwards compatability with older code).
Predictions of type="terms"
are almost invariably passed
forward to further calculation, so for these we default to using
the sample as the reference.
Predictions of type "expected" incorporate the baseline hazard and are
thus absolute instead of relative; the
reference
option has no effect on these.
Models that contain a frailty
term are a special case: due
to the technical difficulty, when there is a newdata
argument the
predictions will always be for a random effect of zero.
a vector or matrix of predictions, or a list containing the predictions (element "fit") and their standard errors (element "se.fit") if the se.fit option is TRUE.
Some predictions can be obtained directly from the coxph object, and for
others it is necessary for the routine to have the entirety of the
original data set, e.g., for type = terms
or if standard errors
are requested.
This extra information is saved in the coxph object if
model=TRUE
, if not the original data is reconstructed.
If it is known that such residuals will be required overall execution will be
slightly faster if the model information is saved.
In some cases the reconstruction can fail.
The most common is when coxph has been called inside another function
and the formula was passed as one of the arguments to that enclosing
function. Another is when the data set has changed between the original
call and the time of the prediction call.
In each of these the simple solution is to add model=TRUE
to the
original coxph call.
options(na.action=na.exclude) # retain NA in predictions fit <- coxph(Surv(time, status) ~ age + ph.ecog + strata(inst), lung) #lung data set has status coded as 1/2 mresid <- (lung$status-1) - predict(fit, type='expected') #Martingale resid predict(fit,type="lp") predict(fit,type="expected") predict(fit,type="risk",se.fit=TRUE) predict(fit,type="terms",se.fit=TRUE)


Website design and development for the pharmaceutical company Farmoquing SAS. This page was designed twice. Developed in JavaScript, PHP, HTML and CSS.
Link to the pageIntern at "Vitalis", an important Colombian pharmaceutical company, helping to design and diagram information on presentations, communications, banners and control panels (BI), to facilitate their understanding and communication to employees. Using tools like "Power BI" and "Adobe Illustrator."
Intern at "Vitalis", an important Colombian pharmaceutical company, assisting in the design and automation of forms through apps for employees. Using Microsoft tools like Power Apps.
Class Assistant of Algorithmic and Object Oriented Programming I and II courses and Introduction to Programming course, being a help for the teacher with the grades and the support to the students in class.
Website design and development for the automotive industry company AJUSTAMOS CAR SYSTEM SAS, in order to fullfill the clients requirements. Developed in JavaScript, PHP, HTML
Link to the page


Whether you're new to coding, or you have some programming courses under your belt, we have something to help you learn this year! Simply submit your email via the form below, and we'll send you some excellent resources to help you become a better coder.


FFMPEG-WEB-TEST
test connect server
tesetconver
task
GIF + mp3 => mp4
time
Duration time.
start time
start time.
upload a GIF
please upload gif file.
上传 mp3
please upload mp3.
START


For about the last four years i have been doing the passion flower thing. This consists of quitting caffine at the beginning of spring at about the time that the passion flower starts to bloom. This is done in order to not have the horrible headaches that you WILL get if you mix the two of these chemicals together.
My method of preparation is pretty simple. I gather the leaves off the side of the road near my house. The flowers taste better, but wiping them out means wiping the plants of the next harvest out. The fruit sucks, don't eat it, it doesn't do anything except make you sick.
After having gathered the passion flower leaves, boil them in some water for a while. Keep an eye on it cause i can't give you exacts on the time and the temp. Strain out the plant material and you have a yellow / brownish tea.   What i suggest (and have used for years now) is getting a frozen concentrate fruit drink and mixing them together using the tea in place of the water in the mix. Got that?
Ok, let's do a run down.
Leaves, Concentrate fruit drink, Pot to boil, strainer, jug, mix concentrate and tea.
I suggest making a lot.
What it does to me: Less of a high and more mood. The first dose was the best because i could feel it hit.  Mixing with marijuana gave myself and several friends a similar effect. Depth perception got messed up in a bizzare way. People, trees, cars, houses, all looked like cardboard standups. Like if you've ever played Doom, it's like how when you walk around items they are the same from every angle, it's like that. When people talk they look like the voice-ins on Forest Gump where the lips are moving.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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| bit0 |
Control of HSync (63.5ps)
0 = IRQ* to CPU disabled interrupt
1 = IRQ* to CPU enabled
|
| bit1 |
Control of interrupt
0 = flag set on falling edge of HSync polarity
1 = flag set on rising edge of HSync
|
| bit2 | Normally 1. 0 = changes $FF00 to data direction |
| bit3 | Sel 1 LSB of two analog MUX select lines |
| bit4 | Always 1 |
| bit5 | Always 1 |
| bit6 | Not used |
| bit7 | Horizontal Sync interrupt flag |


ACADEMICS/FOUNDATION
OUR SERVICES
Regular & Crash Courses Are Available
Eligibility Criteria
- CSBE, ICSE & State Board (Only English Medium)
- 6th to 12th Standard
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- Creative/Advanced Problem Solving
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- Guest Lectures (Motivational, Technical etc.)
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Salient Features
- Providing effective training program and graphical interface on proven success.
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