Miscarriage
- Appointments<PHONE_NUMBER>
- Appointments & Locations
- Contact Us
What is a miscarriage?
A miscarriage, also called a spontaneous abortion, is the spontaneous ending of a pregnancy About 1/3 to 1/2 of all pregnancies end in miscarriage before a woman misses a menstrual period or even knows she is pregnant. About 10-20% of women who know they are pregnant will miscarry.
A miscarriage is most likely to occur within the first 3 months of pregnancy, before 20 weeks’ gestation. Only 1% of miscarriages occur after 20 weeks’ gestation. These are termed late miscarriages.
What are the risk factors for a miscarriage?
A risk factor is a trait or behavior that increases a person’s chance of developing a disease or predisposes a person to a certain condition. Risk factors for miscarriage include:
- Maternal age: Studies show that the risk of miscarriage is 12% to 15% for women in their 20s and rises to about 25% for women at age 40. The increased incidence of chromosomal abnormalities contributes to the age-related risk of miscarriage.
- Certain health conditions in the mother as listed in the section, "What causes miscarriage?"
What causes miscarriage?
About half of all miscarriages that occur in the first trimester are caused by chromosomal abnormalities — which might be hereditary or spontaneous — in the father’s sperm or the mother’s egg. Chromosomes are tiny structures inside the cells of the body that carry many genes, the basic units of heredity.
Genes determine all of a person’s physical attributes, such as sex, hair and eye color, and blood type. Most chromosomal problems occur by chance and are not related to the mother’s or father’s health.
Miscarriages are also caused by a variety of unknown and known factors, such as:
- Infection.
- Exposure to environmental and workplace hazards such as high levels of radiation or toxic agents.
- Hormonal irregularities.
- Improper inplantation of fertilized egg in the uterine lining.
- Maternal age.
- Uterine abnormalities.
- Incompetent cervix. (The cervix begins to widen and open too early, in the middle of pregnancy, without signs of pain or labor.)
- Lifestyle factors such as smoking, drinking alcohol, or using illegal drugs.
- Disorders of the immune system including lupus, an autoimmune disease.
- Severe kidney disease.
- Congenital heart disease.
- Diabetes that is not controlled.
- Thyroid disease.
- Radiation.
- Certain medicines, such as the acne drug Accutane®.
- Severe malnutrition.
- Group B beta strep.
Note: There is no proof that stress, or physical or sexual activity causes miscarriage.
Sometimes, treatment of a mother’s illness can improve the chances for a successful pregnancy.
What are the symptoms of a miscarriage?
Symptoms of a miscarriage include:
- Bleeding that progresses from light to heavy.
- Cramps.
- Abdominal pain.
- Low back ache that may range from mild to severe.
If you are experiencing the symptoms listed above, contact your healthcare provider right away. He or she will tell you to come in to the office or go to the emergency room.
What are some of the symptoms after a miscarriage?
Spotting and mild discomfort are common symptoms after a miscarriage. If you have heavy bleeding, fever, chills, or pain, contact your healthcare provider immediately as these may be signs of an infection.
- Appointments<PHONE_NUMBER>
- Appointments & Locations
- Contact Us


Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.
SAND 2012-7422P Copyright © {{ site.time | date: "%Y" }} {% if site.author %} {{ site.author }} {% else %} {{ site.title }} {% endif %}


Scrum is a framework that helps teams work together. Scrum encourages teams to learn through experiences, self-organize while working on a problem, and reflect on their wins and losses to continuously improve.
A sprint is the actual time period when the scrum team works together to finish an increment. Two weeks is a pretty typical length for a sprint, though some teams find a week to be easier to scope or a month to be easier to deliver a valuable increment.


0.0.1
Add either missing chirality of diastereotopic missing chirality The problem is that sometimes we need to add chiral bond that was not planned because it is the same group This is the case for example for the valine where the 2 C of the methyl groups are diastereotopic
Returns an array of diastereotopic ID (as oclCode)
(OCL.Molecule)
(any)
(OCL.Molecule)
Returns a SVG
(any)
(any
= {}
)
Returns various information about atoms in the molecule
(OCL.Molecule?)
Returns a connectivity matrix
(OCL.Molecule)
(object
= {}
)
| Name | Description |
|---|---|
options.pathLength boolean
(default false )
|
get the path length between atoms |
options.mass boolean
(default false )
|
set the nominal mass of the atoms on diagonal |
options.atomicNo boolean
(default false )
|
set the atomic number of the atom on diagonal |
options.sdt boolean
(default false )
|
set 1, 2 or 3 depending if single, double or triple bond |
Check if a specific atom is a sp3 carbon
(OCL.Molecule)
(number)
Tag an atom to be able to visualize it
(OCL.Molecule)
(number)
Returns an array containing one entry per atom containing diaID and hose code
(OCL.Molecule)
(object)
Returns the hose code for a specific atom number
Returns the hose code for a specific atom number
(OCL.Molecule)
(any)
(any)
(any)
Returns the hose code for a specific marked atom
(OCL.Molecule)
(object
= {}
)


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Vehicle
Upto 8 hrs
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16-24 hrs
Monthly
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2
3
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5
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function Next: Boolean;
Retrieves the next entry in the pointer map and saves a copy to the Data and Item property. Returns True if the operation succeeded, False if there is no more entry to fetch.
[See Also]


com.example1.locationapp
public static final class R.styleable extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
static int[] |
AdsAttrs
Attributes that can be used with a AdsAttrs.
|
static int |
AdsAttrs_adSize |
static int |
AdsAttrs_adSizes |
static int |
AdsAttrs_adUnitId |
static int[] |
MapAttrs
Attributes that can be used with a MapAttrs.
|
static int |
MapAttrs_cameraBearing
This symbol is the offset where the
R.attr.cameraBearing
attribute's value can be found in the MapAttrs array. |
static int |
MapAttrs_cameraTargetLat
This symbol is the offset where the
R.attr.cameraTargetLat
attribute's value can be found in the MapAttrs array. |
static int |
MapAttrs_cameraTargetLng
This symbol is the offset where the
R.attr.cameraTargetLng
attribute's value can be found in the MapAttrs array. |
static int |
MapAttrs_cameraTilt
This symbol is the offset where the
R.attr.cameraTilt
attribute's value can be found in the MapAttrs array. |
static int |
MapAttrs_cameraZoom
This symbol is the offset where the
R.attr.cameraZoom
attribute's value can be found in the MapAttrs array. |
static int |
MapAttrs_mapType
This symbol is the offset where the
R.attr.mapType
attribute's value can be found in the MapAttrs array. |
static int |
MapAttrs_uiCompass
This symbol is the offset where the
R.attr.uiCompass
attribute's value can be found in the MapAttrs array. |
static int |
MapAttrs_uiRotateGestures
This symbol is the offset where the
R.attr.uiRotateGestures
attribute's value can be found in the MapAttrs array. |
static int |
MapAttrs_uiScrollGestures
This symbol is the offset where the
R.attr.uiScrollGestures
attribute's value can be found in the MapAttrs array. |
static int |
MapAttrs_uiTiltGestures
This symbol is the offset where the
R.attr.uiTiltGestures
attribute's value can be found in the MapAttrs array. |
static int |
MapAttrs_uiZoomControls
This symbol is the offset where the
R.attr.uiZoomControls
attribute's value can be found in the MapAttrs array. |
static int |
MapAttrs_uiZoomGestures
This symbol is the offset where the
R.attr.uiZoomGestures
attribute's value can be found in the MapAttrs array. |
static int |
MapAttrs_useViewLifecycle
This symbol is the offset where the
R.attr.useViewLifecycle
attribute's value can be found in the MapAttrs array. |
static int |
MapAttrs_zOrderOnTop
This symbol is the offset where the
R.attr.zOrderOnTop
attribute's value can be found in the MapAttrs array. |
static int[] |
PullToRefreshHeader
Attributes that can be used with a PullToRefreshHeader.
|
static int |
PullToRefreshHeader_ptrHeaderBackground |
static int |
PullToRefreshHeader_ptrHeaderHeight |
static int |
PullToRefreshHeader_ptrHeaderTitleTextAppearance |
static int |
PullToRefreshHeader_ptrProgressBarColor |
static int |
PullToRefreshHeader_ptrProgressBarHeight |
static int |
PullToRefreshHeader_ptrProgressBarStyle |
static int |
PullToRefreshHeader_ptrPullText |
static int |
PullToRefreshHeader_ptrRefreshingText |
static int |
PullToRefreshHeader_ptrReleaseText |
static int[] |
PullToRefreshView
Attributes that can be used with a PullToRefreshView.
|
static int |
PullToRefreshView_ptrViewDelegateClass |
static int[] |
SmoothProgressBar
Attributes that can be used with a SmoothProgressBar.
|
static int |
SmoothProgressBar_spb_color
This symbol is the offset where the
R.attr.spb_color
attribute's value can be found in the SmoothProgressBar array. |
static int |
SmoothProgressBar_spb_colors
This symbol is the offset where the
R.attr.spb_colors
attribute's value can be found in the SmoothProgressBar array. |
static int |
SmoothProgressBar_spb_interpolator
This symbol is the offset where the
R.attr.spb_interpolator
attribute's value can be found in the SmoothProgressBar array. |
static int |
SmoothProgressBar_spb_mirror_mode
This symbol is the offset where the
R.attr.spb_mirror_mode
attribute's value can be found in the SmoothProgressBar array. |
static int |
SmoothProgressBar_spb_reversed
This symbol is the offset where the
R.attr.spb_reversed
attribute's value can be found in the SmoothProgressBar array. |
static int |
SmoothProgressBar_spb_sections_count
This symbol is the offset where the
R.attr.spb_sections_count
attribute's value can be found in the SmoothProgressBar array. |
static int |
SmoothProgressBar_spb_speed
This symbol is the offset where the
R.attr.spb_speed
attribute's value can be found in the SmoothProgressBar array. |
static int |
SmoothProgressBar_spb_stroke_separator_length
This symbol is the offset where the
R.attr.spb_stroke_separator_length
attribute's value can be found in the SmoothProgressBar array. |
static int |
SmoothProgressBar_spb_stroke_width
This symbol is the offset where the
R.attr.spb_stroke_width
attribute's value can be found in the SmoothProgressBar array. |
static int |
SmoothProgressBar_spbStyle
This symbol is the offset where the
R.attr.spbStyle
attribute's value can be found in the SmoothProgressBar array. |
| Constructor and Description |
|---|
R.styleable() |
public static final int[] AdsAttrs
Includes the following attributes:
| Attribute | Description |
|---|---|
| The size of the ad. |
| A comma-separated list of the supported ad sizes. |
| The ad unit ID. |
AdsAttrs_adSize
,
AdsAttrs_adSizes
,
AdsAttrs_adUnitId
public static final int AdsAttrs_adSize
public static final int AdsAttrs_adSizes
public static final int AdsAttrs_adUnitId
public static final int[] MapAttrs
Includes the following attributes:
MapAttrs_cameraBearing
,
MapAttrs_cameraTargetLat
,
MapAttrs_cameraTargetLng
,
MapAttrs_cameraTilt
,
MapAttrs_cameraZoom
,
MapAttrs_mapType
,
MapAttrs_uiCompass
,
MapAttrs_uiRotateGestures
,
MapAttrs_uiScrollGestures
,
MapAttrs_uiTiltGestures
,
MapAttrs_uiZoomControls
,
MapAttrs_uiZoomGestures
,
MapAttrs_useViewLifecycle
,
MapAttrs_zOrderOnTop
public static final int MapAttrs_cameraBearing
This symbol is the offset where the R.attr.cameraBearing
attribute's value can be found in the MapAttrs
array.
Must be a floating point value, such as "1.2
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int MapAttrs_cameraTargetLat
This symbol is the offset where the R.attr.cameraTargetLat
attribute's value can be found in the MapAttrs
array.
Must be a floating point value, such as "1.2
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int MapAttrs_cameraTargetLng
This symbol is the offset where the R.attr.cameraTargetLng
attribute's value can be found in the MapAttrs
array.
Must be a floating point value, such as "1.2
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int MapAttrs_cameraTilt
This symbol is the offset where the R.attr.cameraTilt
attribute's value can be found in the MapAttrs
array.
Must be a floating point value, such as "1.2
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int MapAttrs_cameraZoom
This symbol is the offset where the R.attr.cameraZoom
attribute's value can be found in the MapAttrs
array.
Must be a floating point value, such as "1.2
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int MapAttrs_mapType
This symbol is the offset where the R.attr.mapType
attribute's value can be found in the MapAttrs
array.
Must be one of the following constant values.
| Constant | Value | Description |
|---|---|---|
none | 0 | |
normal | 1 | |
satellite | 2 | |
terrain | 3 | |
hybrid | 4 |
public static final int MapAttrs_uiCompass
This symbol is the offset where the R.attr.uiCompass
attribute's value can be found in the MapAttrs
array.
Must be a boolean value, either "true
" or "false
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int MapAttrs_uiRotateGestures
This symbol is the offset where the R.attr.uiRotateGestures
attribute's value can be found in the MapAttrs
array.
Must be a boolean value, either "true
" or "false
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int MapAttrs_uiScrollGestures
This symbol is the offset where the R.attr.uiScrollGestures
attribute's value can be found in the MapAttrs
array.
Must be a boolean value, either "true
" or "false
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int MapAttrs_uiTiltGestures
This symbol is the offset where the R.attr.uiTiltGestures
attribute's value can be found in the MapAttrs
array.
Must be a boolean value, either "true
" or "false
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int MapAttrs_uiZoomControls
This symbol is the offset where the R.attr.uiZoomControls
attribute's value can be found in the MapAttrs
array.
Must be a boolean value, either "true
" or "false
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int MapAttrs_uiZoomGestures
This symbol is the offset where the R.attr.uiZoomGestures
attribute's value can be found in the MapAttrs
array.
Must be a boolean value, either "true
" or "false
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int MapAttrs_useViewLifecycle
This symbol is the offset where the R.attr.useViewLifecycle
attribute's value can be found in the MapAttrs
array.
Must be a boolean value, either "true
" or "false
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int MapAttrs_zOrderOnTop
This symbol is the offset where the R.attr.zOrderOnTop
attribute's value can be found in the MapAttrs
array.
Must be a boolean value, either "true
" or "false
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int[] PullToRefreshHeader
Includes the following attributes:
| Attribute | Description |
|---|---|
| A drawable to use as the background of the Header View |
| The height of the Header View |
| The Text Appearance of the Header View's Title text |
| The color that the Progress Bar should be tinted with |
| The height of the Progress Bar |
| The style of the Progress Bar |
| Text to show to prompt the user is pull (or keep pulling) |
| Text to show to tell the user that a refresh is currently in progress |
| Text to show to tell the user has scrolled enough to refresh |
PullToRefreshHeader_ptrHeaderBackground
,
PullToRefreshHeader_ptrHeaderHeight
,
PullToRefreshHeader_ptrHeaderTitleTextAppearance
,
PullToRefreshHeader_ptrProgressBarColor
,
PullToRefreshHeader_ptrProgressBarHeight
,
PullToRefreshHeader_ptrProgressBarStyle
,
PullToRefreshHeader_ptrPullText
,
PullToRefreshHeader_ptrRefreshingText
,
PullToRefreshHeader_ptrReleaseText
public static final int PullToRefreshHeader_ptrHeaderBackground
public static final int PullToRefreshHeader_ptrHeaderHeight
public static final int PullToRefreshHeader_ptrHeaderTitleTextAppearance
public static final int PullToRefreshHeader_ptrProgressBarColor
public static final int PullToRefreshHeader_ptrProgressBarHeight
public static final int PullToRefreshHeader_ptrProgressBarStyle
public static final int PullToRefreshHeader_ptrPullText
public static final int PullToRefreshHeader_ptrRefreshingText
public static final int PullToRefreshHeader_ptrReleaseText
public static final int[] PullToRefreshView
Includes the following attributes:
| Attribute | Description |
|---|---|
| The class to use as a View's View Delegate. |
public static final int PullToRefreshView_ptrViewDelegateClass
public static final int[] SmoothProgressBar
Includes the following attributes:
SmoothProgressBar_spbStyle
,
SmoothProgressBar_spb_color
,
SmoothProgressBar_spb_colors
,
SmoothProgressBar_spb_interpolator
,
SmoothProgressBar_spb_mirror_mode
,
SmoothProgressBar_spb_reversed
,
SmoothProgressBar_spb_sections_count
,
SmoothProgressBar_spb_speed
,
SmoothProgressBar_spb_stroke_separator_length
,
SmoothProgressBar_spb_stroke_width
public static final int SmoothProgressBar_spbStyle
This symbol is the offset where the R.attr.spbStyle
attribute's value can be found in the SmoothProgressBar
array.
Must be a reference to another resource, in the form "@[+][package:]type:name
"
or to a theme attribute in the form "?[package:][type:]name
".
public static final int SmoothProgressBar_spb_color
This symbol is the offset where the R.attr.spb_color
attribute's value can be found in the SmoothProgressBar
array.
Must be a color value, in the form of "#rgb
", "#argb
",
"#rrggbb
", or "#aarrggbb
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int SmoothProgressBar_spb_colors
This symbol is the offset where the R.attr.spb_colors
attribute's value can be found in the SmoothProgressBar
array.
Must be a reference to another resource, in the form "@[+][package:]type:name
"
or to a theme attribute in the form "?[package:][type:]name
".
public static final int SmoothProgressBar_spb_interpolator
This symbol is the offset where the R.attr.spb_interpolator
attribute's value can be found in the SmoothProgressBar
array.
Must be one of the following constant values.
| Constant | Value | Description |
|---|---|---|
spb_interpolator_accelerate | 0 | |
spb_interpolator_linear | 1 | |
spb_interpolator_acceleratedecelerate | 2 | |
spb_interpolator_decelerate | 3 |
public static final int SmoothProgressBar_spb_mirror_mode
This symbol is the offset where the R.attr.spb_mirror_mode
attribute's value can be found in the SmoothProgressBar
array.
Must be a boolean value, either "true
" or "false
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int SmoothProgressBar_spb_reversed
This symbol is the offset where the R.attr.spb_reversed
attribute's value can be found in the SmoothProgressBar
array.
Must be a boolean value, either "true
" or "false
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int SmoothProgressBar_spb_sections_count
This symbol is the offset where the R.attr.spb_sections_count
attribute's value can be found in the SmoothProgressBar
array.
Must be an integer value, such as "100
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int SmoothProgressBar_spb_speed
This symbol is the offset where the R.attr.spb_speed
attribute's value can be found in the SmoothProgressBar
array.
Must be a floating point value, such as "1.2
".
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int SmoothProgressBar_spb_stroke_separator_length
This symbol is the offset where the R.attr.spb_stroke_separator_length
attribute's value can be found in the SmoothProgressBar
array.
Must be a dimension value, which is a floating point number appended with a unit such as "14.5sp
".
Available units are: px (pixels), dp (density-independent pixels), sp (scaled pixels based on preferred font size),
in (inches), mm (millimeters).
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.
public static final int SmoothProgressBar_spb_stroke_width
This symbol is the offset where the R.attr.spb_stroke_width
attribute's value can be found in the SmoothProgressBar
array.
Must be a dimension value, which is a floating point number appended with a unit such as "14.5sp
".
Available units are: px (pixels), dp (density-independent pixels), sp (scaled pixels based on preferred font size),
in (inches), mm (millimeters).
This may also be a reference to a resource (in the form
"@[package:]type:name
") or
theme attribute (in the form
"?[package:][type:]name
")
containing a value of this type.


Introduction
Welcome to the Openframe JavaScript Client! You can use this lib to access Openframe REST API endpoints, which can be used to fetch and update artworks, frames, and profile information for authenticated users.
At present we only have this JavaScript client library, but you’re welcome to use any language to interact with the API directly.
All of the methods of the JS client return Promises which are resolved with the parsed response body or are rejected with an error message.
Initializing the JS Client
import OF from 'openframe-jsclient';
// use default openframe server, openframe.io
const OF = new OF();
// to use a different server, pass option to constructor
const OF = new OF({
api_base: 'http://localhost:8888/api'
});
Import the Javascript client class and create a new instance, optionally passing configuration options.
Configuration Options
| Option | Default | Description |
|---|---|---|
| api_base | https://api.openframe.io/ | The base URL where the API is located |
Authentication / Authorization
Although some Openframe data is public and can be retrieved via unauthenticated access, the API requires an authenticated user’s access token to be supplied with requests in order to manipulate a user’s data. An access token can be obtained by hitting the login
endpoint and providing a valid username (or email) and password.
Once obtained, the access token may be included in requests as a query param:
GET https://api.openframe.io/v0/user/12345/owned_frames?access_token=wtu2jsJYZTO8ZqjGokR1ejznxCw4Qd0hACFo50GXyx3eGcVNNroccDWHZHmHVXKn
or in an Authentication header:
Authentication: wtu2jsJYZTO8ZqjGokR1ejznxCw4Qd0hACFo50GXyx3eGcVNNroccDWHZHmHVXKn
As noted below, after successful login, the JS client manages storage and inclusion of the header (in browsers with localStorage available).
Log in
This assumes you’ve got an instantiated OF client.
OF.users.login({'username': 'test', 'password': 'test'})
.then(token => {
console.log(token);
});
// or
OF.users.login({'email'<EMAIL_ADDRESS>'password': 'test'})
.then(token => {
console.log(token);
});
If valid credentials are passed, the resolved token object, looks like this, where “id” is the actual access token value:
{
"id": "wtu2jsJYZTO8ZqjGokR1ejznxCw4Qd0hACFo50GXyx3eGcVNNroccDWHZHmHVXKn",
"ttl": 1209600,
"created": "2016-12-05T03:45:55.936Z",
"userId": "56c47fba45e503657a51bebc"
}
On success, the login
endpoint responds with an access token that is valid for two weeks or until explicity destroyed via logout. The token is presented as the id
value of response body, and must be supplied with subsequent requests on behalf of the logged in user.
If localStorage is present (i.e. the browser), the JS client automatically saves the access token to localStorage and includes it in subsequent responses.
Log out
Calling logout
will destroy the access token on the server and, if present, clear it from localStorage. Successful logout will result in a 204 no content response and resolve the Promise. If there is any problem destroying the access token on the server, the promise will be rejected with an error.
OF.users.logout()
.then(() => {
// success, no response body
})
.catch(error => {
console.log(error);
});
Users
Fetch a list of Users
OF.users.fetch(filter)
.then(users => {
// do something with users
});
The above users array is structured like this:
[
{
"full_name": "Sol Lewitt",
"username": "slewitt",
"created": "2016-02-17T14:12:10.463Z",
"id": "56c47fba45e503657a51bebd",
"modified": "2016-02-17T14:12:10.463Z"
},
{
"full_name": "Peter Pan",
"username": "ppan",
"created": "2016-02-17T14:12:10.560Z",
"id": "56c47fba45e503657a51bebc",
"modified": "2016-02-17T14:12:10.560Z"
},
{
"full_name": "Missy Elliot",
"username": "melliot",
"created": "2016-02-17T14:12:10.666Z",
"id": "56c47fba45e503657a51bebe",
"modified": "2016-02-17T14:12:10.666Z"
}
]
This endpoint retrieves a list of users. At present, limited information is public for all Openframe users.
Arguments
| Arg | Default | Description |
|---|---|---|
| filter | {} | A filter config object |
Fetch a User by ID
OF.users.fetchById()
.then(user => {
// user is currently authenticated user
});
OF.users.fetchById("56c47fba45e503657a51bebd")
.then(user => {
// do something with user
});
The above user object is structured like this:
{
"full_name": "Sol Lewitt",
"username": "slewitt",
"created": "2016-02-17T14:12:10.463Z",
"id": "56c47fba45e503657a51bebd",
"modified": "2016-02-17T14:12:10.463Z"
}
This method retrieves a specific user by ID.
If no ID is passed, it defaults to ‘current’, which returns the currently authenticated user.
Arguments
| Arg | Default | Description |
|---|---|---|
| ID | 'current’ | A user ID |
Fetch a User by username
OF.users.fetchByUsername("slewitt")
.then(user => {
/* user looks like
{
"full_name": "Sol Lewitt",
"username": "slewitt",
"created": "2016-02-17T14:12:10.463Z",
"id": "56c47fba45e503657a51bebd",
"modified": "2016-02-17T14:12:10.463Z"
}
*/
});
This method retrieves a specific user by Username.
Arguments
| Arg | Default | Description |
|---|---|---|
| username | none | A username |
Frames
Fetch all current User’s frames
Artwork
Fetch a list of Artworks
OF.artwork.fetch(filter)
.then(artworks => {
// do something with artworks
});
The above artworks array is structured like this:
[
{
"title": "unknown-1471551583623.frag",
"is_public": true,
"url": "https://thebookofshaders.com/log/160818203933.frag",
"thumb_url": "https://thebookofshaders.com/log/160818203933.png",
"author_name": "unknown",
"required_extensions": {},
"format": "openframe-glslviewer",
"id": "57b61d12c0006da8310e9143",
"ownerId": "57b61cf8c0006da8310e9141",
"created": "2016-08-18T20:39:46.662Z",
"modified": "2016-08-18T20:39:46.662Z"
},
{
"title": "Nutrition Facts",
"is_public": true,
"url": "http://streetkonect.com/nutritionfacts",
"thumb_url": "http://streetkonect.com/nutritionfacts/nf4b.jpg",
"author_name": "Leah Valle",
"required_extensions": {},
"format": "openframe-website",
"id": "57a85d5bc0006da8310e906b",
"ownerId": "57a85cf2c0006da8310e9069",
"created": "2016-08-08T10:22:19.405Z",
"modified": "2016-08-08T10:22:19.405Z"
}
]
This endpoint retrieves a list of Artworks. If called by an unauthenticated user, the list will include only public Artworks. If called by an authenticated user, private Artworks which the user has added will also be present in the response.
Fetch an Artwork by ID
Filtering queries
// an example filter for an OF.users.fetch(filter)
let filter = {
fields: ['username', 'id', 'website'], // fields to include
include: 'created_artwork', // include a relation in the result
limit: 10, // limit the number of results
skip: 10, // skip a number of results
order: 'username ASC', // result order direction, ASC or DESC
where: { // filter by field data
username: {
like: 'abc' // lots of operator options
}
},
}
All of the fetch
methods can take an optional filter
config object, which allows filtering of the query results. Whenever available, it takes the form shown on the right.
Errors
The Kittn API uses the following error codes:
| Error Code | Meaning |
|---|---|
| 400 | Bad Request – Your request sucks |
| 401 | Unauthorized – Your API key is wrong |
| 403 | Forbidden – The kitten requested is hidden for administrators only |
| 404 | Not Found – The specified kitten could not be found |
| 405 | Method Not Allowed – You tried to access a kitten with an invalid method |
| 406 | Not Acceptable – You requested a format that isn’t json |
| 410 | Gone – The kitten requested has been removed from our servers |
| 418 | I’m a teapot |
| 429 | Too Many Requests – You’re requesting too many kittens! Slow down! |
| 500 | Internal Server Error – We had a problem with our server. Try again later. |
| 503 | Service Unavailable – We’re temporarially offline for maintanance. Please try again later. |


Award
Serena O’Dwyer from Cashel receives Gold Gaisce Award
The awards were presented by President Michael D Higgins in a prestigious ceremony hosted by Sinead Burke
by Tipperary Star reporterThe President of Ireland's award ceremony took place in Dublin Castle.
The awards were presented by President Michael D Higgins in a prestigious ceremony hosted by Sinead Burke.
Serena O’Dwyer received her bronze and silver award as a pupil of Cashel Community School and started her Gaisce journey to the gold award in sixth year under the guidance of her President Award Leader Irene Codd.
The Gold Award requires participants to undertake five challenges.
Serena volunteered in Uganda for the residential element.
For her adventure journey she undertook a four-day walk along the Wild Atlantic Way, she volunteered as a mentor in a Big Brother Big Sister program where she assisted a young lady from a disadvantaged background.
To keep fit for her physical section Serena chose dancing and finally for her personal skill she took singing lessons.
Cashel Community School would like to congratulate Serena on this fantastic achievement. Her tenacity to complete all the requirements of the award is to be commended.
The Gaisce Gold Award challenge is not an easy one – it demands motivation, determination and focus over a minimum of 52 weeks but in most cases takes a number of years to complete.
This achievement not only demonstrates a personal determination, but also a commitment to the service of her community.


Ajibola Kola is a result-oriented and enthusiastic tech-savvy. I am a Front-End Web Developer/IT Support Specialist, and I build my world around knowledge acquisition. I am also into Digital Marketing as I believe it is the most obvious way of getting oneself or idea into the world.
Let's Socialize


a crash course cheat sheet & web site for alchemy in skyrim
brought to you by a fellow player who loves the game; distributed under the
unlicense
ingredient list
potions for power leveling
favorite potions for combat


About FlippingBook for Online Newsletters
The main problem with PDF
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The only thing the marketer needs is to take the link and paste it into the email. Their clients will be able to open the newsletter everywhere and have the best experience with it.
FlippingBook doesn’t require any special knowledge or coding skills, it’s a perfect tool for designers and marketers who wish to provide their audience with good-looking and engaging newsletters.


The Canary Set for Machine Learning Applications
Earlier this week I was chatting to some people and we came up with a good idea. It’s what I refer to now as the “Canary Set”.
We’re probably all familiar with the standard “canary in the cage” idea:
The standard canary in a cage.
It’s an item that can be carried with you into your hazardous job, and when it dies, it’s suggestive that the place your working in is unsafe. Poor canary.
I’d like to propose that this can be a useful idea in machine learning, where instead of a canary, we carry around an “interesting set” of data:
A Canary Set of interesting data points.
This would be called the “Canary Set” of data, and every time a new deployment of your ML model comes out, you run the mdoel across this dataset to see how it performs.
Going in …
Coming out with a report …
Let’s go into a few details of why we might want this, how it differs from the “test” or “validation” set during the training/evaluation phase of machine learning algorithms, and what benefits we might get from it.
The Canary Set: Main Justification
The main point of the canary set is to give users of your machine learning service control over a set of data that they want to evaluate your new models against. In this way, it differs significantly from the standard “test” set in that it doesn’t need to contain a well-distributed set of samples, it just needs to contain those samples that the user is interested in.
These might be:
- Rare inputs,
- Inputs for which they are uniquely concerned, as compared to other users,
- “Extreme” inputs, that check areas they are interested in,
- A random selection of their past inputs,
- … many more ideas!
I think there’s a world of options here. The point is: The user gets some control. And this is a nice idea, given that we want them to be gaining confidence in the ML system.
Secondary benefits
Increasingly we’re going to see that there will be different environments for ML training and inference. We’ve already explored this in our last blog post on TensorFlow.js, but there are also the growing number of so-called “edge devices”, the Google Edge TPU, the Movidius and the JeVois, among others.
The point is, we can’t actually guarantee that when we do testing of our model during training that it will agree exactly with the inference world, because there may be many stages in between.
This actually came up on a project I was working on last year. We trained a model, and when we deployed it to the cloud, it didn’t work well. We tracked it down to a bug in the GPU code relating to some post-processing of the models output. This error was only found becaues I decided it would be a good idea to test the model on some “real” data, before making it live. In other words, I made up my own canary set and tested the model on that first, before making it live.
Conclusion
In summary, the idea is that in any machine learning service, for users, that you build, you could consider adding the facility for the users to be involved in selecting a set of data that is used to evaluate any new versions of the models.
The benefits are:
- Increasing user engagement and trust,
- finding new interesting edge-cases where your algorithm fails,
- and ensuring the quality of inference when you may not be in control of the inference endpoint.
Hope this idea is helpful!


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Complaining Of Treatment In New York, Trump Announces Residency Change To Florida
President Donald Trump announced Thursday that he’d filed paperwork to change his primary residence from Manhattan to Palm Beach, Florida, griping about his treatment at the hands of New York officials.
According to the New York Times, he and Melania Trump submitted documents to change their primary residence in late September, filing a “declaration of domicile” for Mar-a-Lago.
At least one New York official is not sad to see him go:


{% extends "base.html" %} {% block main %}
Create beautiful wallpapers by tiling your favorite patterns. You can find free patterns online at https://www.subtlepatterns.com.


|
BitShares-Core
6.1.0
BitShares blockchain implementation and command-line interface software
|
#include <api_connection.hpp>
Public Member Functions | |
| api_connection (uint32_t max_depth) | |
| virtual | ~api_connection () |
| template<typename T > | |
| api< T > | get_remote_api (api_id_type api_id=0) |
| virtual variant | send_call (api_id_type api_id, string method_name, variants args=variants())=0 |
| virtual variant | send_callback (uint64_t callback_id, variants args=variants())=0 |
| virtual void | send_notice (uint64_t callback_id, variants args=variants())=0 |
| variant | receive_call (api_id_type api_id, const string &method_name, const variants &args=variants()) const |
| variant | receive_callback (uint64_t callback_id, const variants &args=variants()) const |
| void | receive_notice (uint64_t callback_id, const variants &args=variants()) const |
| template<typename Interface > | |
| api_id_type | register_api (const Interface &a) |
| template<typename Signature > | |
| uint64_t | register_callback (const std::function< Signature > &cb) |
| std::vector< std::string > | get_method_names (api_id_type local_api_id=0) const |
Public Attributes | |
| fc::signal< void()> | closed |
| const uint32_t | _max_conversion_depth |
Definition at line 240 of file api_connection.hpp.
|
inline |
Definition at line 243 of file api_connection.hpp.
|
inlinevirtual |
Definition at line 244 of file api_connection.hpp.
|
inline |
Definition at line 295 of file api_connection.hpp.
|
inline |
Definition at line 248 of file api_connection.hpp.
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inline |
Definition at line 260 of file api_connection.hpp.
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inline |
Definition at line 265 of file api_connection.hpp.
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inline |
Definition at line 270 of file api_connection.hpp.
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inline |
Definition at line 277 of file api_connection.hpp.
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inline |
Definition at line 289 of file api_connection.hpp.
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pure virtual |
makes calls to the remote server
Implemented in fc::local_api_connection, fc::rpc::cli, and fc::rpc::websocket_api_connection.
|
pure virtual |
Implemented in fc::local_api_connection, fc::rpc::cli, and fc::rpc::websocket_api_connection.
|
pure virtual |
Implemented in fc::local_api_connection, fc::rpc::cli, and fc::rpc::websocket_api_connection.
| const uint32_t fc::api_connection::_max_conversion_depth |
Definition at line 298 of file api_connection.hpp.
| fc::signal<void()> fc::api_connection::closed |
Definition at line 297 of file api_connection.hpp.


1984 A film review by Ted Prigge Copyright 1997 Ted Prigge
Director: Michael Radford Writer: Michael Radford (Based on the novel by George Orwell) Starring: John Hurt, Suzanna Hamilton, Richard Burton, Cyril Cusack
George Orwell is probably one of the greatest writers of all time. And his "1984" is probably his best (although I might argue it could be "Animal Farm"). The novel, written around 1948, I think, is a bleak story of a future where there is peace but the expense is a totalitarian state where everyone is miserable and afraid because there are TVs everywhere where "Big Brother" watches so you don't do anything treasonous. It's a frightening idea and pretty much illustrates that "Give Peace a Chance" might be a bad idea.
Michael Radford (director of "Il Postino") directed this film, ironically made in 1984 within the dates the book took place. He paints a futuristic world which doesn't resemble anything else before. There are no flying cars, monorails, or anything. The world is just a ruin with run-down buildings and plain old buses and what-not. In every room, there is a big television where Big Brother, the symbol of the state, is watching and the Thought Police are always the threat of even thinking of a treasonous act.
Our protagonist is Winston Smith (John Hurt), who really looks like he has been beaten down by the state in all senses of the word. He is a state worker who rewrites history the way the government wants it and in his spare time, writes in a journal about his dreams of having rampant sex, which is prohibited. One of his co-workers, Parsons (Gregor Fisher), is the symbol of the ultimate defeat of the state, since he is constantly telling of his love for everything that embodies Big Brother. In one darkly humorous scene, he talks about the processed meat and how it isn't even meat and that's why it's good.
Winston runs into (literally) a woman named Julia (Suzanna Hamilton) who's also a government worker and who passes him a note (Hello? Junior High?) saying she loves him and wants to meet him.  Their sex is not actually about love, but as a form of rebellion and expression of their way to anti-supress themselves. They try to join the rebellion, headed by an O'Brien (Richard Burton, in his final performance), but that turns out to wind them up in prison and a torture sequence on Winston where he is beaten into admitting that 2 + 2 doesn't equal 4 and stuff like that. It's a horrific scene in all senses of the word.
"1984" the film works because it wonderfully embodies the bleakness that was the book. All of the scenes are drearily set and the film is as depressing and thought-provoking as the book...well, maybe not as thought-provoking. And John Hurt and Richard Burton are fabulous in their respective parts.
I highly reccomend this film for anyone who loved the book, but for anyone else, it's not going to be fun. It's a highly depressing film that is so dark that you might have to watch it in two sittings. But the message is still there and that's what counts. If you want a livelier film (and a better one, in my opinion), try Terry Gilliam's "Brazil." But this one will do if you're looking for a good intellectual film.
MY RATING (out of 5): ****1/2
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


We manually review every order to protect us from fraud. So, account setup can take 12hrs time but but usually much quicker.
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class: center, middle, inverse, title-slide # Introduction to Statistics ## Part III: Introduction to Regression and Pitfalls of Statistical Analysis ### Stéphane Guerrier & Yuming Zhang ### 22 January 2021 --- # Example: Reading ability assessment .panelset[ .panel[.panel-name[Problem] .smallest[An educator believes that new directed reading activities in the classroom can help elementary school students (6-12 years old) improve their reading ability. She arranged a pilot study where some students (chosen at random) of age 6 start to take part in these activities .hi-pink[(treatment group)], meanwhile other students continue with the classical curriculum .hi-purple[(control group)]. After three years, the educator wishes to evaluate the effectiveness of these activities so all students are given a Degree of Reading Power (DRP) test, which assesses their reading ability with scores given below: ] ```r treatment_score = c(43, 48, 41, 51, 46, 58, 49, 54, 45, 52, 49, 42, 50, 52, 62, 54, 65, 58, 52, 69) control_score = c(35, 43, 45, 50, 50, 46, 56, 63, 55, 63, 58, 74, 60, 70, 72, 75, 69, 70, 73, 71) ``` .smallest[.pink[Can we conclude that these new directed reading activities can help elementary school students improve their reading ability?]] ] .panel[.panel-name[Boxplot]  ] .panel[.panel-name[Boxplot Code] ```r boxplot(treatment_score, control_score, names = c("Treatment", "Control"), ylab = "Score of the DRP test", col = c("#F8766D", "#00BFC4")) points(1:2, c(mean(treatment_score), mean(control_score)), pch = 16, col = 1) ``` ] .panel[.panel-name[Test] 1. .purple[Define hypotheses:] `\(H_0: \mu_t = \mu_c\)` and `\(H_a: \mu_t > \mu_c\)`. 2. .purple[Define] `\(\color{#6A5ACD}{\alpha}\)`: We consider `\(\alpha = 5\%\)`. 3. .purple[Compute p-value]: p-value = `\(99.11\%\)` (see R code tab for details). 4. .purple[Conclusion:] We have p-value > `\(\alpha\)` so we cannot reject the null hypothesis at the significance level of 5%. .hi-pink[Remark:] Notice that the p-value for the test with opposite hypotheses is actually `\(1-99.11\%=0.89\% < \alpha\)`. So we conclude, at the significance level of 5%, that the classical curriculum without these new directed reading activities actually improves students' reading ability more compared to the curriculum with these activities. 🤔 ] .panel[.panel-name[`R` Code ] ```r t.test(treatment_score, control_score, alternative = "greater") ``` ``` #> #> Welch Two Sample t-test #> #> data: treatment_score and control_score #> t = -2.4989, df = 31.844, p-value = 0.9911 #> alternative hypothesis: true difference in means is greater than 0 #> 95 percent confidence interval: #> -13.25578 Inf #> sample estimates: #> mean of x mean of y #> 52.0 59.9 ``` ] ] --- # Is our analysis comprehensive? The educator points out that she has considered these new directed reading activities for only three years. So only students of 6-8 years old have participated in these activities. In other words, in the sample she collected, the students in the treatment group are only of age 6 to 8, whereas the students in the control group vary from 6 to 12 years old. .pink[Is age a potential explanation to the difference we observe among the students' reading ability?] To make sure that the analysis is reliable, she includes the age information of the students as follows: ```r treatment_age = c(6.60, 6.11, 6.02, 6.06, 6.01, 6.33, 6.17, 6.21, 6.63, 6.06, 7.10, 7.17, 7.20, 7.34, 7.54, 7.80, 7.86, 8.27, 8.58, 8.89) control_age = c(6.42, 6.02, 7.23, 7.51, 8.01, 8.12, 8.09, 8.22, 8.37, 9.10, 9.29, 10.09, 10.35, 10.21, 10.17, 11.54, 11.28, 12, 12.11, 12.24) ``` --- # Should age be taken into account?  --- # Regression models - .smallest[.hi-pink[Regression models] study the effects of some explanatory variables (also called covariates or regressors)] `\(\small X_1, \ldots, X_p\)` .smallest[on a response variable] `\(\small Y\)` .smallest[of primary interest.] - .smallest[The relationship between the response variable] `\(\small Y\)` .smallest[and the covariates is not deterministic. Instead, it shows some random errors. In other words, the response] `\(\small Y\)` .smallest[is random and .purple[its mean value is modelled as a function of the covariates.]] - .smallest[In .hi-pink[linear regression models], the mean value of] `\(\small Y\)` .smallest[is a linear combination of the covariates:] `$$Y = \beta_0 + \beta_1 X_1 + \ldots + \beta_p X_p + \epsilon, \quad \epsilon \sim \mathcal{N}(0, \sigma^2).$$` - .smallest[.purple[The random errors are assumed to be independently and identically normally distributed with mean 0 and variance]] `\(\small \color{#6A5ACD}{\sigma^2}\)` .smallest[.purple[.]] - .smallest[The parameters of interest are] `\(\small \beta_0, \beta_1, \ldots, \beta_p\)`.smallest[👋].smallest[. They are estimated using the least squares approach.] .footnote[.smallest[👋 Here we consider a simple situation where the variance is known. In practice, however, the variance is typically unknown so it needs to be estimated as well.]] --- # Example: Reading ability assessment In the reading ability example, we can formulate a linear regression model as follows: `$$\color{#e64173}{\text{Score}_i} = \beta_0 + \beta_1 \color{#6A5ACD}{\text{Group}_i} + \beta_2 \color{#20B2AA}{\text{Age}_i} + \epsilon_i, \quad \epsilon_i \sim \mathcal{N}(0, \sigma^2).$$` - `\(\color{#e64173}{\text{Score}_i}\)`: score of the DRP test of the `\(i\)`-th student. - `\(\color{#6A5ACD}{\text{Group}_i}\)`: indicator of participation in the new directed reading activities for the `\(i\)`-th student (i.e. `\(\color{#6A5ACD}{\text{Group}_i} = 1\)` if participate and `\(\color{#6A5ACD}{\text{Group}_i} = 0\)` if not participate). - `\(\color{#20B2AA}{\text{Age}_i}\)`: age of the `\(i\)`-th student. With this model the two groups can be compared as the age effect is taken into account. The goal of the educator is now to assess if `\(\beta_1\)` is .pink[significantly larger than 0] (why??? 🤔). --- # Example: Reading ability assessment .panelset[ .panel[.panel-name[R Code] ```r # combine all available data together reading_data = data.frame(score = c(treatment_score, control_score), group = c(rep(1,20), rep(0, 20)), age = c(treatment_age, control_age)) # fit a linear regression model mod = lm(score ~ group + age, data = reading_data) summary(mod) ``` ] .panel[.panel-name[Output] ``` #> #> Call: #> lm(formula = score ~ group + age, data = reading_data) #> #> Residuals: #> Min 1Q Median 3Q Max #> -10.9494 -3.8379 -0.7843 4.5170 9.8537 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 8.611 5.918 1.455 0.1541 #> group 4.875 2.313 2.108 0.0419 * #> age 5.504 0.620 8.878 1.06e-10 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 5.727 on 37 degrees of freedom #> Multiple R-squared: 0.7256, Adjusted R-squared: 0.7108 #> F-statistic: 48.92 on 2 and 37 DF, p-value: 4.074e-11 ``` ] .panel[.panel-name[Graph Code] ```r par(mai = c(2, 2, 1, 1)) plot(treatment_age, treatment_score, pch = 16, col = "#F8766D", xlim = c(6, 12.5), ylim = c(30, 85), xlab = "Age (in years)", ylab = "Score of the DRP test") points(control_age, control_score, pch = 17, col = "#00BFC4") lines(6:12, coef(mod)[1]+coef(mod)[2]*1+coef(mod)[3]*(6:12), col = "#F8766D") lines(6:12, coef(mod)[1]+coef(mod)[2]*0+coef(mod)[3]*(6:12), col = "#00BFC4") legend("bottomright", legend = c("Treatment", "Control"), col = c("#F8766D", "#00BFC4"), bty = "n", pch = c(16, 17)) ``` ] .panel[.panel-name[Graph]  ] ] --- # Interpretation of coefficients We can obtain the estimated coefficients. Specifically, - `\(\hat{\beta}_0 = 8.611\)` represents the estimated baseline average score of the DRP test. - `\(\hat{\beta}_1 = 4.875\)` means that .pink[for a student of the same age], participating in the new directed reading activities is estimated to increase their average score of the DRP test by 4.875. - `\(\hat{\beta}_2 = 5.504\)` means that .pink[when a student receives the same treatment] (either participate or not in the activities), their average score increases by 5.504 as they become 1 year older. Regression coefficients represent the mean change in the response variable .purple[for one unit of change] in the predictor variable .purple[while holding other covariates in the model constant.] --- # Interpretation of coefficient p-values - We notice that for each coefficient `\(\beta_i\)`, there is a corresponding p-value. - This coefficient p-value is associated to the test of `\(H_0: \beta_i = 0\)` and `\(H_a: \beta_i \neq 0\)`. - .pink[A covariate with a small p-value (typically smaller than 5%) is considered to be a significant (meaningful) addition to the model], as changes in the values of such covariate can lead to changes in the response variable. - On the other hand, a large p-value (typically larger than 5%) suggests that the corresponding covariate is not significantly associated with changes in the response. - In this example, the coefficient p-value associated to the "group" covariate is 4.19% (smaller than 5%). .purple[This suggests that taking into account the effect of age, the reading abilities of the students receiving the treatment are significantly .hi.purple[different] from the control group, at the significance level of 5%.] But this is not what we want! 😤 --- # Interpretation of coefficient p-values .smaller[In the linear regression output, the coefficient p-value (which we denote as] `\(\small p\)`.smaller[) corresponds to a two-sided test. We can use this result to compute the p-value of a one-sided test using the following relations:] | | `\(\small H_a: \beta_i>0\)` | `\(\small H_a: \beta_i<0\)` | | ------------- |:-------------:| :-----:| | `\(\small \hat{\beta_i}>0\)` | `\(\small p/2\)` | `\(\small 1-p/2\)` | | `\(\small \hat{\beta_i}<0\)` | `\(\small 1-p/2\)` | `\(\small p/2\)` | .smaller[In our example,] `\(\small \beta_1 = 4.875\)` .smaller[and] `\(\small p=4.19\%\)`.smaller[. So the p-value of the test with hypotheses] `\(\small H_0: \beta_1=0\)` .smaller[and] `\(\small H_a: \beta_1>0\)` .smaller[is] `\(\small 4.19\% /2 = 2.095\% < \alpha\)`.smaller[. .pink[So we can conclude that these new directed reading activities can significantly improve students' reading ability compared to classical curriculum.] 😍] --- # Interpretation of R-squared - The .hi-pink[coefficient of determination], denoted as `\(R^2\)` and often referred to as R-squared, is the proportion of the variance in the response variable that is predictable from the covariates. - `\(\color{#6A5ACD}{R^2}\)` .purple[gives certain information about the goodness of fit of a model.] It measures how well the regression predictions approximate the real data points. An `\(R^2\)` of 1 indicates that the regression predictions perfectly fit the data. - However, adding new covariates to the current model .purple[always] increases `\(R^2\)`, whether the additional covariates are significant or not. Therefore, `\(R^2\)` alone cannot be used as a meaningful comparison of models with different covariates. - The .hi-pink[adjusted] `\(\color{#e64173}{R^2}\)` is a modification of `\(R^2\)` that aims to limit this issue. --- # Exercise: Is gender also significant? .panelset[ .panel[.panel-name[Problem] .pink[The educator thinks that the reading ability may also vary between male and female students.] She further provided the gender information of students, which can be imported as follows: ```r # gender information: 1 = female, 0 = male treatment_gender = c(1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1) control_gender = c(0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0) # combine all available data together reading_data = data.frame(score = c(treatment_score, control_score), group = c(rep(1,20), rep(0, 20)), age = c(treatment_age, control_age), gender = c(treatment_gender, control_gender)) ``` ] .panel[.panel-name[R Code] ```r # fit a linear regression model with added "gender" covariate mod = lm(score ~ group + age + gender, data = reading_data) summary(mod) ``` ] .panel[.panel-name[Output] ``` #> #> Call: #> lm(formula = score ~ group + age + gender, data = reading_data) #> #> Residuals: #> Min 1Q Median 3Q Max #> -9.4898 -3.8595 -0.1481 3.2879 11.2247 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 6.1217 5.9726 1.025 0.3122 #> group 5.1266 2.2650 2.263 0.0297 * #> age 5.6125 0.6093 9.212 5.31e-11 *** #> gender 2.9567 1.7795 1.662 0.1053 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 5.595 on 36 degrees of freedom #> Multiple R-squared: 0.7451, Adjusted R-squared: 0.7239 #> F-statistic: 35.09 on 3 and 36 DF, p-value: 8.754e-11 ``` ] ] --- # How to make predictions? .smallest[Given the promising improvement on students' reading ability with these new directed reading activities, the educator plans to continue involving her students to participate in these activities. She is interested in predicting .pink[how a student, who follows curriculum with these activities, will perform in the DRP test at the end of their elementary school studies (i.e. at 12 years old)?]] .smallest[In general, to predict the value of the response variable for a future observation with the value] `\(\small x_i^*\)` .smallest[of the explanatory variables, we use] `$$\small \hat{y}^* = \hat{\beta}_0 + \hat{\beta}_1 x_1^* + \ldots + \hat{\beta}_p x_p^*.$$` .smallest[In this case, recall that with the model] `\(\small \color{#e64173}{\text{Score}} = \beta_0 + \beta_1 \color{#6A5ACD}{\text{Group}} + \beta_2 \color{#20B2AA}{\text{Age}} + \epsilon\)`, .smallest[we have] `\(\small \hat{\beta}_0 = 8.611\)`, `\(\small \hat{\beta}_1 = 4.875\)` .smallest[and] `\(\small \hat{\beta}_2 = 5.504\)`.smallest[. So the predicted score of the DRP test for a 12 year-old student with participation in these activities can be computed as] `$$\small 8.611 + 4.875 \cdot \color{#6A5ACD}{1} + 5.504 \cdot \color{#20B2AA}{12} \approx \color{#e64173}{79.53}.$$` --- # Uncertainty of predictions - Similar to point estimation, prediction is also .hi-turquoise[random] as it relies on estimation `\(\hat{\beta}_i\)` that is computed on a random sample. - Moreover, there exists a variability of the future response around the mean due to the unavoidable random errors of the model. - To measure the uncertainty of the prediction, we use the .hi-pink[prediction intervals]. - Prediction intervals correspond to .purple[a range of values that are likely to include the true value of a future observation.] It includes a margin of error to indicate its accuracy. The level of accuracy is a probability expressed as a percentage (%). - A 95% prediction interval means that the probability of using this model to produce an interval that contains the true value of the future observation is 95%. --- # Prediction intervals .panelset[ .panel[.panel-name[Problem] The educator is interested in comparing how a student will perform in the DRP test at the end of the elementary school studies (i.e. at 12 years old), with and without these new directed reading activities, with probability of 95%. We predict, with the probability of 95%, that for 12 year-old students who particiate in these activities, their scores of the DRP test may fall in the range of .pink[(66.09, 92.98)]. On the other hand, for those who do not participate in these activities, their scores may fall in the range of .purple[(62.30, 87.02)]. (See R code tab for details.) ] .panel[.panel-name[R Code] ```r # with treatment mod = lm(score ~ group + age, data = reading_data) new_data = data.frame(group = 1, age = 12) predict(mod, new_data, interval = "predict") ``` ``` #> fit lwr upr #> 1 79.5338 66.08554 92.98206 ``` ```r # without treatment mod = lm(score ~ group + age, data = reading_data) new_data = data.frame(group = 0, age = 12) predict(mod, new_data, interval = "predict") ``` ``` #> fit lwr upr #> 1 74.659 62.30138 87.01661 ``` ] ] --- # Limitations of linear regression models - .smaller[Linear regression .pink[assumes a linear relationship] between the response and the covariates. However this is sometimes incorrect.] - .smaller[Linear regression .pink[is applicable when the response variable is continuous and approximately normally distributed.] So it is not suitable when, for example, we need to analyze binary data or count data. In this case, a more general class of model called the .purple[generalized linear model] can be used.] - .smaller[Linear regression .pink[only considers independent data.] So it is not suitable, for example, for time dependent data or clustered data. For these cases, we may use .purple[time series analysis] or .purple[mixed effect model] respectively.] - .smaller[Linear regression .pink[should not be used to extrapolate], i.e. to estimate beyond the original observation range. For example, if we consider a 100 year-old person in this reading ability example, we would predict that the corresponding scores of the DRP test would be 563.89 and 559.01, respectively, with and without these activities. Does it really make sense? 😯] --- # Limitations of linear regression models  👋 .smallest[If you want to know more have a look [here](https://www.explainxkcd.com/wiki/index.php/605:_Extrapolating).] --- # Pitfalls of statistical analysis .smallest[.hi-pink[P-Hacking] refers to the misuse of data analysis to find patterns in data that can be presented as statistically significant. .turquoise[This is done by performing many statistical tests on the data and only reporting those that come back with significant results].smallest[👋]. .hi-purple[Example]: a correlation between the number of letters in [Scripps National Spelling Bee's](https://en.wikipedia.org/wiki/Scripps_National_Spelling_Bee) winning word and the number of people in the United States killed by venomous spiders.]  .smallest[Source: [Wikipedia on Data dredging ](https://en.wikipedia.org/wiki/Data_dredging)]. 👋 .smallest[If you want to know more, have a look [here](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1124898/).] --- # How to P-Hack? 🤔 .smaller[Are republicans good or bad for the American Economy? .hi-purple[Both!!] (see [link](https://projects.fivethirtyeight.com/p-hacking/))]  --- # This brings us to "HARKing" .pull-left[.smallsest[.hi-pink[HARKing] is an acronym coined by Norbert Kerr for the .purple[questionable research practice of "Hypothesizing After the Results are Known"]. It can be defined as presenting .hi-turquoise[a post hoc] hypothesis in the introduction of a research report as if it were an .hi-turquoise[a priori] hypothesis. .hi-pink[How to do it?] 🤔 - Test if one jelly bean color is linked to acne. - Pretend this was our original hypothesis. - Et voilà! 👋 If you want to know more, have a look [here](https://en.wikipedia.org/wiki/HARKing) and the references therein.]] .pull-right[
  .tiny[Source: [xkcd](https://xkcd.com/882/)] ] --- # The costs of HACKing (and P-Hacking) HARKing is (arguably) playing an important role in the .hi-purple[replication crisis in science]!
  .tiny[Source: [Open Science Collaboration. "Estimating the reproducibility of psychological science." Science 349.6251 (2015)](https://science.sciencemag.org/content/sci/349/6251/aac4716.full.pdf?casa_token=2i_9tsA7a-QAAAAA:B-N4J8bk1hBRboZAmPKuythdhFcdhKKNOtBATZX1PD69qhV-cl1zZG3zcd25LkhnKMpRrmO9TJAxo3f8).] 👋 .smallest[If you want to know more, have a look [here](https://en.wikipedia.org/wiki/Replication_crisis) and the references therein.] --- # ⚠ Take home message - Any data analysis made on a sample of data is subject to .purple[randomness]. - Understanding the random processes underlying any data analysis, including underlying statistics such as confidence intervals and p-values, requires basic knowledge in probability and statistics, or .pink[statistical literacy]. - With data, there is never 100% certainty, but rather a .pink[conclusion associated to a (estimated) risk] that the conclusion might be wrong. - .purple[One has to live with this fact, and any scientific activity that might conclude with 100% certainty on the basis of data, is simply a fallacy]. - However, properly controlling for the statistical risk and properly stating the conclusions that can be drawn from a data analysis, can really bring new knowledge, especially in all the sciences that are based on observations.


enum class Direction
Whether the inputs or outputs list contains an encumbrance issue, see TransactionMissingEncumbranceException.
Issue in the inputs list.
Issue in the outputs list.


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Contiki 2.5
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Files | |
| file | assert.c [code] |
| file | assert.h [code] |
| file | checkpoint.c [code] |
Checkpoint library. | |
| file | checkpoint.h [code] |
Checkpoint library header. | |
| file | crc16.c [code] |
Implementation of the CRC16 calculcation. | |
| file | crc16.h [code] |
Header file for the CRC16 calculcation. | |
| file | ctk-filedialog.c [code] |
| file | ctk-filedialog.h [code] |
| file | ctk-textentry-checkbox.c [code] |
| file | ctk-textentry-checkbox.h [code] |
| file | ctk-textentry-cmdline.c [code] |
| file | ctk-textentry-cmdline.h [code] |
| file | ctk-textentry-multiline.c [code] |
| file | ctk-textentry-multiline.h [code] |
| file | gcr.c [code] |
Implementation of GCR coding/decoding. | |
| file | gcr.h [code] |
| file | ifft.c [code] |
| file | ifft.h [code] |
| file | libconio.c [code] |
| file | libconio.h [code] |
| file | list.c [code] |
Linked list library implementation. | |
| file | list.h [code] |
Linked list manipulation routines. | |
| file | me.c [code] |
Implementation of the table-driven Manchester encoding and decoding. | |
| file | me.h [code] |
Header file for the table-driven Manchester encoding and decoding. | |
| file | me_tabs.c [code] |
| file | me_tabs.h [code] |
| file | memb.c [code] |
Memory block allocation routines. | |
| file | memb.h [code] |
Memory block allocation routines. | |
| file | mmem.c [code] |
Implementation of the managed memory allocator. | |
| file | mmem.h [code] |
Header file for the managed memory allocator. | |
| file | petsciiconv.c [code] |
| file | petsciiconv.h [code] |
PETSCII/ASCII conversion functions. | |
| file | print-stats.c [code] |
Convenience function for printing system statistics. | |
| file | print-stats.h [code] |
Convenience function for printing system statistics. | |
| file | random.c [code] |
| file | random.h [code] |
| file | ringbuf.c [code] |
Ring buffer library implementation. | |
| file | ringbuf.h [code] |
Header file for the ring buffer library. | |
| file | sensors.c [code] |
| file | sensors.h [code] |
| file | strncasecmp.c [code] |


We were inspired by personal expierences, and this is a problem that many people dont want to talk about.We came together to create this website as a way to address the topic in a comfortable format that everyone can understand.
Journey to Diverse City is on a mission to educate those who are ignorant to racism and stereotypes through skits, news,personal stories, games, and getting them involved.


Pitfalls¶
This page lists issues which may arise while using statsmodels. These can be the result of data-related or statistical problems, software design, “non-standard” use of models, or edge cases.
statsmodels provides several warnings and helper functions for diagnostic checking (see this blog article for an example of misspecification checks in linear regression). The coverage is of course not comprehensive, but more warnings and diagnostic functions will be added over time.
While the underlying statistical problems are the same for all statistical packages, software implementations differ in the way extreme or corner cases are handled. Please report corner cases for which the models might not work, so we can treat them appropriately.
Repeated calls to fit with different parameters¶
Result instances often need to access attributes from the corresponding model instance. Fitting a model multiple times with different arguments can change model attributes. This means that the result instance may no longer point to the correct model attributes after the model has been re-fit.
It is therefore best practice to create separate model instances when we want to fit a model using different fit function arguments.
For example, this works without problem because we are not keeping the results instance for further use
mod = AR(endog)
aic = []
for lag in range(1,11):
res = mod.fit(maxlag=lag)
aic.append(res.aic)
However, when we want to hold on to two different estimation results, then it is recommended to create two separate model instances.
mod1 = RLM(endog, exog)
res1 = mod1.fit(scale_est='mad')
mod2 = RLM(endog, exog)
res2 = mod2.fit(scale_est=sm.robust.scale.HuberScale())
Unidentified Parameters¶
Rank deficient exog, perfect multicollinearity¶
Models based on linear models, GLS, RLM, GLM and similar, use a generalized inverse. This means that:
- Rank deficient matrices will not raise an error
- Cases of almost perfect multicollinearity or ill-conditioned design matrices might produce numerically unstable results. Users need to manually check the rank or condition number of the matrix if this is not the desired behavior
Note: Statsmodels currently fails on the NIST benchmark case for Filip if the data is not rescaled, see this blog
Incomplete convergence in maximum likelihood estimation¶
In some cases, the maximum likelihood estimator might not exist, parameters might be infinite or not unique (e.g. (quasi-)separation in models with binary endogenous variable). Under the default settings, statsmodels will print a warning if the optimization algorithm stops without reaching convergence. However, it is important to know that the convergence criteria may sometimes falsely indicate convergence (e.g. if the value of the objective function converged but not the parameters). In general, a user needs to verify convergence.
For binary Logit and Probit models, statsmodels raises an exception if perfect prediction is detected. There is, however, no check for quasi-perfect prediction.
Other Problems¶
Insufficient variation in the data¶
It is possible that there is insufficient variation in the data for small datasets or for data with small groups in categorical variables. In these cases, the results might not be identified or some hidden problems might occur.
The only currently known case is a perfect fit in robust linear model estimation. For RLM, if residuals are equal to zero, then it does not cause an exception, but having this perfect fit can produce NaNs in some results (scale=0 and 0/0 division) (issue #55).


wakanda
Data Driven
Web Development
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Developer Advocate
@lyle
Chickens!
Wakanda Application Framework
wakandaDB
Web Server
&
Database
as a Webserver
Traditional Multi-Threaded
Server Side JavaScript
Automatic RESTful API
as a Database
Relational (multi-table)
Accessed through JavaScript Objects
Indexed and Transactional
4D Inc
Creates Development Tools
For Data-Rich Applications
4D is Sponsoring...
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SNEAKERS [*SPOILERS*] A film review by Donn B. Parker Copyright 1992 Donn B. Parker
*SPOILERS* *SPOILERS* *SPOILERS* *SPOILERS* *SPOILERS*
[Extracted from comp.risks:
[The following review was prepared by Donn Parker for distribution to the members of the International Information Integrity Institute (known as I-4), an organization consisting of something like 60 companies with significant interest in improved computer security and integrity, which is managed by SRI -- with Donn as one of its key players. This review is reproduced here with his permission, and is authorized for further distribution, with appropriate attribution. PGN] -Moderator]
FILM REVIEW OF SNEAKERS by Donn B. Parker September 1992
SNEAKERS (released September 11, 1992 by Universal Studios, owned by Matsushita Electric Industrial Co., Ltd., and promoted in association with CompuServe, owned by H&R Block) starring Robert Redford, Dan Aykroyd, Ben Kingsley, Mary McDonnell, River Phoenix, Sidney Poitier, and David Strathairn; directed by Phil Alden Robinson (Field of Dreams director); and produced and written by Walter F. Parkes and Lawrence Lasker (writers and producers of WARGAMES in 1981).
The new computer crime movie, SNEAKERS (as in hackers who wear sneakers and sneak into computers) was previewed in a San Francisco showing sponsored by Universal Studios and Mondo 2000 Magazine (a slick-cover psychedelic publication of the Timothy Leary genre appealing to hackers) and attended by a large segment of the Bay Area hacker community including Cap'n Crunch. I had assisted the writers, Messrs. Lasker and Parkes, with their first movie, WARGAMES--much to my chagrin because the technology was so distorted. This time they had the technical assistance of Len Adelman (the A in RSA Crypto) from UCLA and Robert Abbott, an information security consultant of long standing. This "Mission Impossible," PG-rated (only three "God damn"s and almost no sex) film is mostly technologically believable, unlike WARGAMES. We can forgive them for showing a Cray computer with a terminal displaying Windows 3.1.
All information security professionals should see this film and use it to promote security awareness. Some critics may pan it, but it has all the ingredients for financial success. It has:
o great chase and other street action scenes in the beautiful San Francisco Bay Area o an interesting but predictable plot o the blind technician who finds the bad guys' hideout from sounds heard from the trunk of a car o the old technique of the bad guy shooting into the ceiling tiles at his hidden enemy hiding in ceiling duct area o three bloodless murders o total unconsciousness produced by simple taps on the head followed by immediate concussion-less revival with little visible damage o popular stars, Redford, Poitier, Aykroyd, and Kingsley, who look like the oldest hackers in the world (except for me and Cap'n Crunch) o great human melodrama with good character development and not too much technical sci-fi stuff o the good guy and his girlfriend at the mercy of the bad guy in the grand finale o cryptography very well explained and used for a general audience o the proverbial spinning computer tape drive, and o as usual with Lasker and Parkes, a moral at the end.
Universal has uniquely teamed up with CompuServe and CompUSA computer stores to promote the movie. A chat board has been set up to fire questions about the movie at Mr. Robinson, the director, who has been using CompuServe for 8 years. Anagram and secret password games can be played, with prizes including trips to Hollywood and Robert Redford's jacket worn in the film. The film is sure to be a big hit in Europe and Japan as well as in the United States and should appeal to the juvenile hacker culture throughout the world.
One unbelievable item is the skimpy $175,000 accepted by Redford's security penetration (read "tiger team") consulting company for a record-breaking information security project. Redford's team plus all the high-priced technical equipment were worth much more than that. They had to steal the universal decryption black box-the Maltese Falcon of the movie-and then steal it again from the bad guys posing as NSA types who steal it from Redford. There is a neat shoulder-surfing password pickup by video recording. There are hacker antics such as a transfer of President Nixon's net worth to the National Organization for the Reform of Marijuana Laws (NORML), credit record and license plate registration privacy invasions, trashing of the NSA, CIA, and FBI, and liberal-politics slams at President Bush and the Republican Party well-timed for the upcoming national elections. However, this is all tolerable since it is done by Redford's character and his team who all have serious criminal and other highly unethical practices in their backgrounds.
A tiger team attack on a client bank that has relatively good security is excessively elaborate and would have left the bank guard in a good position to sue his employer for aggravated assault and mental anguish. We will probably have to assure our company management people that we don't do things like that-but the time to justify your budget and staff is soon after they see this movie.
The film ends with the rather patronizing and simplistic advice that whoever controls the information, controls the world. Just the straightforward action and technology without all the liberal politics and moralizing would have made it even better.
You and your teenage children and your computer users and management should all see and enjoy this much-to-be-talked-about film.
.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


sealed class ContractLog
Base wrapper class for contract result types
ContractLogx86 |
Result type for x86 contract class ContractLogx86 : ContractLog |
RegularContractLog |
Result type for regular contract class RegularContractLog : ContractLog |
address |
var address: String |


Admin JSON Editor
This is a live view of your data, you can edit it in realtime or add new field/values.
field
:
val
add


The Crowsnest Pass is a unified community of five formerly separate towns (Blairmore, Bellevue, Coleman, Hillcrest, and Frank) cradled in a narrow stretch of lush valley in Southern Alberta's Rocky Mountains. It is an idyllic natural setting of mountains, rolling hills and winding rivers. The natural environment nurtures a genuine mountain lifestyle. An abundance of outdoor activities await you including snowmobiling, snowshoeing, cross country skiing, downhill skiing, hiking, mountain biking, caving, fishing and hunting.


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Liaison Is Now Called Layr
You will be redirected to the
new website
.
Visit the new website


{% extends 'base.html' %} {% block content %}
| {{ user_email.email }} |
{% if user.email == user_email.email %}
Primary
{% else %}
{% include 'fragments/accounts/delete_email_form.html' with email=user_email.email %}
{% if user_email.is_confirmed %}
{% include 'fragments/accounts/set_primary_email_form.html' with email=user_email.email %}
{% else %}
(Not verified)
{% endif %}
{% endif %}
|


| About OpenWSMAN |
Openwsman is an open source implementation of WS-Management; enabling the
in-band management of Linux/uni*/Windows platforms. Openwsman supports the generic WS-Management protocol as well as specific protocol extensions for the Common Information Model (CIM) |
| About WS-Management |
Web Services for Management (WS-Management, WS-MAN) is a specification for managing
computer systems using Web services standards. WS-Management is a SOAP based transport protocol for managing (create, enumerate, access, change, destroy) resources. |
| Features |
|
| Supported Standards | |
| Documentation | The OpenWSMAN wiki is the primary documentation site. There is also online documentation for the Ruby client bindings |
| Getting help | If your question is not covered in the OpenWSMAN wiki, please subscribe to the openwsman-devel mailing list and ask there. |
| Reporting bugs | Bugs should be reported via the github issue tracker. |
| Related projects |
There are a couple of related projects hosted under the Openwsman github account:
|
| Supported platforms | Openwsman is primarily developed on Linux but is known to run on Windows and MacOS as well. |
|
Downloading OpenWSMAN | |
| Source code |
Tarballs of releases are distributed via GitHub. The latest release is 2.7.2 (Changelog) |
| Git | The git repo for Openwsman is hosted on GitHub. |
| RPM packages | RPM binaries for Linux are available via the openSUSE Build Service |
| Ruby gem | A Ruby GEM is available at rubygems.org |
| Python package |
To build and install a Python package, you should be able to do:
cd bindings/python/pywsman python setup.py sdist # Make a python source distribution cd dist pip install pywsman-*.tar.gz # Build and install the python package |
| Other formats | Other formats (i.e. Debian .deb files) would need a maintainer. Please contact me if you're interested in maintaining other formats. |


 Feeding God’s Children and Feeding God’s Children Marks are registered trademarks of Feeding God’s Children
Feeding God’s Children is a 506(c)93) charitable organization. Feeding God’s Children and Feeding God’s Children Marks are registered trademarks of Feeding God’s Children


Next: print-not-readable-object, Previous: *print-right-margin*, Up: Printer Dictionary
print-not-readable, error, serious-condition, condition, t
The type print-not-readable consists of error conditions that occur during output while *print-readably* is true, as a result of attempting to write a printed representation with the Lisp printer that would not be correctly read back with the Lisp reader. The object which could not be printed is initialized by the :object initialization argument to make-condition, and is accessed by the function print-not-readable-object.


LockEth is a Decentralized Application for storing and managing your passwords in blockchain. Powered
by Ethereum blockchain. LockEth provides users a secured, encrypted & tamper-proof way for storing
and managing passwords of various online accounts. It has timer feature to logout automatically after
few minutes.


Jolly brought to Kattappana
Taken to her ancestral house and parents’ house for evidence collection
by A CorrespondentThe special investigation team (SIT) probing the Koodathayi serial murder case brought prime accused Jolly Shaju to Kattappana for evidence collection on Saturday.
She was taken to her ancestral home at Vazhavara and the house at Kattappana town where her father and mother are living at present.
There was a huge crowd at her ancestral home when she was brought there around 10 a.m. A posse of police was stationed there.
Nearly 17 years ago, a pet dog in the house was poisoned to death. It is suspected that her first victim — her mother-in-law Annamma — was killed using the same poison, which is now banned.
Parents’ statements
She was then brought to the current residence of her parents, a two-storey house at Valiyakandam near the town. The SIT team also took the statements of her parents.
Jolly, who had co-operated with the SIT team in collecting evidence hiterto, seemed tense when she reached her ancestral house.


Evaluation Goals
Goals
Ease of Use
- Our first goal is the ease of use of our application. We want to evaluate how easy it is for a user
to navigate, learn, and use our software.
- How easy is our application to learn?
- How easy is our application to use?
System Functionality
- Our second goal is the overall functionality of our application. We want to evaluate how our
application functions as compared to other competing applications.
- Does our application fulfill the functionality requirements of a casual DJ?
Feature Depth
- Our final goal is the feature depth of our application. We want to evaluate whether or not
our application is more than just a DJ tool.
- Do users feel like our application is more than just a novelty item?
- Would users use this often?
Critical Aspects
Drawer
- The buttons, sliders, and the drawer itself
- Evaluation of the buttons and sliders in our drawer in regard to our three
evaluation goals
Playlist Control/Flow
- Creating, changing, and the flow of a playlist of music
- Evaluation of a playlist in regard to our three evaluation goals
Home Screen
- The song information and layout of our Home Screen
- Evaluation of the song info and layout in regard to the three evaluation goals
AutoEQ Screen
- Drawing on the waveform, playing with two songs at once in our AutoEQ screen
- Evaluation of the drawing and playing with the waveform of a song in regard to the
three evaluation goals
Metrics
One to Five Measuring Scale
Similar to the Likert Scale and the One-to-Ten Rating Scale, our rating scale ranges from 1 to 5. One is the lowest score possible
and Five is the highest score a participant can give.
We will be using this scale to measure our evaluation tasks.
EXAMPLE
Rate your level of frustration during this task (1 = no frustration, 5 = most frustration).
1 ---------- 2 ---------- 3 ---------- 4 ----------- 5


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Code of Conduct
All attendees, speakers, sponsors and volunteers at our meetup are required to agree with the following code of conduct. Organisers will enforce this code throughout the event. We expect cooperation from all participants to help ensure a safe environment for everybody.
The Quick Version
Our meetup is dedicated to providing a harassment-free experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion (or lack thereof), or technology choices. We do not tolerate harassment of meetup participants in any form. Sexual language and imagery is not appropriate for any meetup venue, including talks, networking, social events, Twitter and other online media. Meetup participants violating these rules may be sanctioned or expelled from the meetup at the discretion of the meetup organisers.
The Less Quick Version
Purpose
A primary goal of LuxembourgJS is to be inclusive to the largest number of contributors, with the most varied and diverse backgrounds possible. As such, we are committed to providing a friendly, safe and welcoming environment for all, regardless of gender, sexual orientation, ability, ethnicity, socioeconomic status, and religion (or lack thereof). This code of conduct outlines our expectations for all those who participate in our community, as well as the consequences for unacceptable behavior. We will also apply those rules for the selection of speakers, sponsors, and venues. We invite all those who participate in LuxembourgJS to help us create a safe and positive experience for everyone.
Unacceptable behavior
The following behaviors are considered harassment and are unacceptable within our community:
- Discriminatory jokes and language related to gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion (or lack thereof), or technology choices.
- Posting or displaying sexually explicit or violent material.
- Violence, threats of violence or violent language directed against another person.
- Posting or threatening to post other people’s personally identifying information ("doxing").
- Personal insults, particularly those related to gender, sexual orientation, race, religion, or disability.
- Inappropriate photography or recording.
- Inappropriate physical contact. You should have someone’s consent before touching them.
- Unwelcome sexual attention. This includes, sexualized comments or jokes; inappropriate touching, groping, and unwelcomed sexual advances. Our meetups are not dating events.
- Deliberate intimidation, stalking or following (online or in person).
- Advocating for, or encouraging, any of the above behavior.
- Sustained disruption of community events, including talks and presentations.
Consequences of Unacceptable Behavior
Unacceptable behavior from any community member, including sponsors and those with decision-making authority, will not be tolerated. Anyone asked to stop unacceptable behavior is expected to comply immediately. If a participant engages in harassing behavior, the meetup organisers may take any action they deem appropriate, up to and including a temporary ban or permanent expulsion from the community without warning.
Reporting an incident
If you are being harassed, notice that someone else is being harassed, or have any other concerns, please contact one of the meetup organisers immediately.
The organisation team will be happy to help participants contact venue security or local law enforcement, provide escorts, or otherwise assist those experiencing harassment to feel safe during the meetups.
Addressing Grievances
If you feel you have been falsely or unfairly accused of violating this Code of Conduct, you should notify the organisation team with a concise description of your grievance. Your grievance will be handled in accordance with our existing governing policies.
Scope
We expect all participants (attendees; sponsors; speakers; hosts; and other guests) to abide by this Code of Conduct in all community venues–online and in-person–as well as in all one-on-one communications pertaining to community business. This code of conduct and its related procedures also applies to unacceptable behavior occurring outside the scope of community activities when such behavior has the potential to adversely affect the safety and well-being of community members.
Inspired from:
- https://confcodeofconduct.com/
- https://www.ashedryden.com/blog/codes-of-conduct-101-faq
- http://citizencodeofconduct.org/
Published under CC-BY-SA


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Daisy Gardens will seem like a breeze until you are asked to recover a key or a bag of birdseed from some Legal Eagles. Those feathery Lawbots are tricky birds and only nest at the tops of the tallest buildings. Try finding some fellow Toons in the playground who are working on the same task - divide and conquer to find those Lawbot buildings!
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Here is a list of everything you will earn by completing all of the Daisy Gardens tasks:
- A Laff point limit of 43
- The ability to carry 35 gags
- The ability to carry 80 jellybeans
- The ability to carry three ToonTasks
- Teleport access to Daisy Gardens playground
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The EUCAS lab is led by Henry Brighton and located in the Department of Cognitive Science and Artificial Intelligence, Tilburg University, The Netherlands.
Our goal is to advance the understanding of cognitive and adaptive systems by examining how theories, models, and organisms cope with extended uncertainty conditions.
Our research cuts across disciplinary divisions to investigate fundamental questions in the study of rationality, decision science, linguistic theory, economic theory, and evolutionary and adaptive systems. We use of machine learning techniques, statistical modeling, and laboratory and online experiments with human participants to explore these issues.
Please see the Research section for details about our approach and research contributions, and the Publications section for information about specific publications.
The EUCAS Lab recieves funding from KPN in collaboration with The Jheronimus Academy of Data Science (JADS) and the Technical University of Eindhoven.
New working paper open for public peer review in Economics E-Journal
1st October 2018Henry Brighton talks at Rebuilding Macroeconomics, HM Treasury, London.
July 17th 2017Henry Brighton interviewed by US National Public Radio on medical decision making and Google search results.
March 4th 2017Henry Brighton interviewed by Quartz magazine on human rationality.
April 8th 2016Tête-à-tête Berlin interviews Henry Brighton.


Contact us
PHONE: 016 49894 016
EMAIL<EMAIL_ADDRESS>SKYPE: linhyo14293
ADDRESS: Room 706, Building C, Hanoi University
{% extends "base.html" %} {% block content %}
PHONE: 016 49894 016
EMAIL<EMAIL_ADDRESS>SKYPE: linhyo14293
ADDRESS: Room 706, Building C, Hanoi University


With my marketing and branding experience we can build your online identity from the ground up
Your client's will be on the go, let's make sure your website is easy for them to use anywhere & everywhere.
Let's talk to find out a way to bring your vision to life.
I attended New York University which is located in the heart of New York City's Greenwich Village. There I earned my degree in Computer Science with a minor in Web Development!
Out of college I started working at a consulting dev studio named Disrupto based in Flatiron. There I worked on projects with The NY Rangers, NY Knicks, & Samsung. I also helped develop Member.ly, Disrupto's first attempt to create it's own product.!
I started a position with GC, an e-commerce enablement platform, and worked with a team of other Front-end Engineers.
In 2013 I started as a Front-end Engineer with Yext. Where I worked for over 3 years on the Product Delivery Team!
In Jan I switched to the Marketing Team at Yext where I eventually earned the title of Senior Web Developer implementing and maintaining the Marketing Team's Technical Prescence.
I've worked for over 10 years as a software engineer and a web developer which means I can built your website from the ground up.
My philosophy is a website should be accessible, easy to use and beautiful to look at.
Put your website to work! Your website should make your business easier to find and allow your customers to get in touch with you quick.
Let's make your vision a reality. Contact me at<EMAIL_ADDRESS>so we can get your site built today!


For the offshore industry highly accurate, specialised forecasts are given on a frequent basis with access provided to forecasters on a 24 hour basis. In this way offshore installations are able to avoid costly damage due to bad weather by being prepared well in advance. Additionally delicate weather sensitive operations can be carried out when the conditions are most suitable.
For the shipping industry a shiprouteing service, MetROUTE is provided on an international basis enabling voyages to be planned and routed to ensure lowest bunkering costs and fastest times. This service is manned by master mariners with direct experience of a ship's highest priorities.
Other services to the marine industry include specialised forecasts for fishing fleets, ferry services and ports.
For those needing direct access to up-to-date weather information The Met.Office provides MIST, a system which downloads direct to your own PC giving animated satellite and other weather details.
For further information contact:
John Mitchell
Tel.No. +44 (0)1244 211840
Postal Address: Aberdeen Weather Centre, Seaforth Centre, Lime Street, Aberdeen, AB2 1BJ. United Kingdom
or simply fill in our Response Form.


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