|
| Local Methods | ||
| Managed Object Types | Data Object Types | All Properties | All Methods |
Properties
| Name | Type | Description |
|---|---|---|
| None |
Methods
| Methods defined in this Managed Object |
|---|
| AcquireCredentialsInGuest, ReleaseCredentialsInGuest, ValidateCredentialsInGuest |
This can be used to authenticate inside the guest and obtain a GuestAuthentication object for supported authentication types. This operation is not needed for Name and Password Authentication. To use Name and Password Authentication, see NamePasswordAuthentication. For SSPI authentication, requestAuth should be of the type SSPIAuthentication.
Parameters
| Name | Type | Description |
|---|---|---|
| _this | ManagedObjectReference | A reference to the GuestAuthManager used to make the method call. |
| vm P | ManagedObjectReference
to a VirtualMachine |
MoRef of the VM to perform the operation on.
|
| requestedAuth | GuestAuthentication |
The guest authentication data used to acquire credentials.
See GuestAuthentication.
|
| sessionID* | xsd:long |
The sessionID number should be provided only when
responding to a server challenge. The sessionID number to be used with
the challenge is found in the
GuestAuthenticationChallenge object.
|
Return Value
| Type | Description |
|---|---|
| GuestAuthentication | Returns a GuestAuthentication object that can be used in guest operation calls. |
Faults
| Type | Description |
|---|---|
| GuestAuthenticationChallenge | Thrown if the credential information provided requires a challenge to authenticate. |
| GuestComponentsOutOfDate | Thrown if the guest agent is too old to support the operation. |
| GuestOperationsFault | Thrown if there is an error processing a guest operation. |
| GuestOperationsUnavailable | Thrown if the VM agent for guest operations is not running. |
| InvalidGuestLogin | Thrown if the the guest authentication information was not accepted. |
| InvalidPowerState | Thrown if the VM is not powered on. |
| InvalidState | Thrown if the operation cannot be performed because of the virtual machine's current state. |
| OperationDisabledByGuest | Thrown if the operation is not enabled due to guest agent configuration. |
| OperationNotSupportedByGuest | Thrown if the operation is not supported by the guest OS. |
| RuntimeFault | Thrown if any type of runtime fault is thrown that is not covered by the other faults; for example, a communication error. |
| TaskInProgress | Thrown if the virtual machine is busy. |
| TooManyGuestLogons | Thrown if there are too many concurrent login sessions active in the guest. |
Events
| Type | |
|---|---|
| None |
This frees any resources and session data associated with a GuestAuthentication object returned by AcquireCredentialsInGuest. The GuestAuthentication object can no longer be used to authenticate in the guest once released. Currently this operation is only valid for TicketedSessionAuthentication objects.
Parameters
| Name | Type | Description |
|---|---|---|
| _this | ManagedObjectReference | A reference to the GuestAuthManager used to make the method call. |
| vm P | ManagedObjectReference
to a VirtualMachine |
MoRef of the VM to perform the operation on.
|
| auth | GuestAuthentication |
The guest authentication data. See
GuestAuthentication.
|
Return Value
| Type | Description |
|---|---|
| None |
Faults
| Type | Description |
|---|---|
| GuestComponentsOutOfDate | Thrown if the guest agent is too old to support the operation. |
| GuestOperationsFault | Thrown if there is an error processing a guest operation. |
| GuestOperationsUnavailable | Thrown if the VM agent for guest operations is not running. |
| InvalidGuestLogin | Thrown if the the guest authentication information was not accepted. |
| InvalidPowerState | Thrown if the VM is not powered on. |
| InvalidState | Thrown if the operation cannot be performed because of the virtual machine's current state. |
| OperationDisabledByGuest | Thrown if the operation is not enabled due to guest agent configuration. |
| OperationNotSupportedByGuest | Thrown if the operation is not supported by the guest OS. |
| RuntimeFault | Thrown if any type of runtime fault is thrown that is not covered by the other faults; for example, a communication error. |
| TaskInProgress | Thrown if the virtual machine is busy. |
Events
| Type | |
|---|---|
| None |
This can be used to check the authentication data, or validate any authetication that has a timeout is still valid. If the authentication is not valid, GuestPermissionDenied will be thrown.
Parameters
| Name | Type | Description |
|---|---|---|
| _this | ManagedObjectReference | A reference to the GuestAuthManager used to make the method call. |
| vm P | ManagedObjectReference
to a VirtualMachine |
MoRef of the VM to perform the operation on.
|
| auth | GuestAuthentication |
The guest authentication data. See
GuestAuthentication.
|
Return Value
| Type | Description |
|---|---|
| None |
Faults
| Type | Description |
|---|---|
| GuestComponentsOutOfDate | Thrown if the guest agent is too old to support the operation. |
| GuestOperationsFault | Thrown if there is an error processing a guest operation. |
| GuestOperationsUnavailable | Thrown if the VM agent for guest operations is not running. |
| InvalidGuestLogin | Thrown if the the guest authentication information was not accepted. |
| InvalidPowerState | Thrown if the VM is not powered on. |
| InvalidState | Thrown if the operation cannot be performed because of the virtual machine's current state. |
| OperationDisabledByGuest | Thrown if the operation is not enabled due to guest agent configuration. |
| OperationNotSupportedByGuest | Thrown if the operation is not supported by the guest OS. |
| RuntimeFault | Thrown if any type of runtime fault is thrown that is not covered by the other faults; for example, a communication error. |
| TaskInProgress | Thrown if the virtual machine is busy. |
Events
| Type | |
|---|---|
| None |
| Top of page | Local Methods | ||
| Managed Object Types | Data Object Types | All Properties | All Methods |


Jeremiah 10:15
They are vanity, and the work of errors: in the time of their visitation they shall perish.


Managing Pests in Gardens: Vegetables: InvertebratesWireworms
Information about managing pests of gardens and landscapes, from UC IPM. . Damage. Wireworm larvae injure sprouting seeds and seedlings by feeding on .
http://www.ipm.ucdavis.edu/PMG/GARDEN/VEGES/PESTS/wireworm.html
Treatment for Herniated or Bulging Discs
Wireworm, Wireworms: Gardener's Supply
Wireworm feeding can destroy corn seeds before they germinate, deform the roots of beets and carrots, and damage potato tubers. Wireworms are the larvae of .
http://www.gardeners.com/Wireworm/5311,default,pg.html
Wireworm Control
Description: Common in home gardens across North America, wireworms (up to 1-1/2 inch long) are tough slender worms with shiny skin and three pairs of . Larvae feed underground for 2-6 years with most of their damage occurring in early .
http://www.planetnatural.com/site/xdpy/kb/wireworm-control.html
wire worm damage in garden Herniated or Bulging Discs
wireworm, wireworm damage, click beetle, Agriotes lineatus ...
The Worrisome Lowdown on the Wiley Wireworm. Wireworm damage in root crops can be damned annoying and frustrating for the home gardener, and plain .
http://www.certifiedorganic.bc.ca/rcbtoa/training/wireworm.htm
Wireworms
above soil level. Wireworm damage is often worst in cool, wet spring weather. . purposes and are not meant to be used in home gardens. Many home uses of .
http://extension.usu.edu/htm/publications/file=5345
Gardening Tips: Protect Potatoes From Wireworms
Although wireworms feed predominantly on potato plants, don't forget that your other garden vegetables are at risk as well. If you find wireworm damage in your .
http://www.essortment.com/gardening-tips-protect-potatoes-wireworms-53403.html
Official Impairment Listing for Spinal Disorders
How to Get Rid of Wire Worms in a Garden | eHow.com
The wireworm itself can almost be more disastrous to your crops and garden . to remove or prevent the wireworms as it could severely damage your garden.
http://www.ehow.com/how_5530318_rid-wire-worms-garden.html
- north star la jolla (narrowing of the spine)
Wireworms & Click Beetles - WSU Whatcom County Extension
Wireworm larvae look like tubular, caramelized noodles that are about an inch long . Damage: The larvae of these two species prefer to feed on grass seeds and . wireworms feed on the developing root systems and tubers of garden grains, .
http://whatcom.wsu.edu/ag/homehort/pest/wireworm.htmWireworms Fact sheet - Rutgers New Jersey Agricultural Experiment ...
Wireworm and damage to lima bean. Wireworm and . Wireworm larvae eat small roots, burrow into tubers and fleshy . Insect Pests of the Home Garden Series .
http://njaes.rutgers.edu/pubs/download-free.asp?strPubID=FS294
Wireworm Damage Information On The Control Of Wireworms
By Susan Patterson, Master Gardener. Wireworms are a major source of grief amongst corn farmers. They can be very destructive and difficult to control.
http://www.gardeningknowhow.com/plant-problems/pests/controlling-wireworm-pests.htm
Wireworm - Gardening - About.com
Damage from Wireworms. Wireworms feed on a wide range of plants. Commercially, they are a big pest of corn and potatoes, but in home gardens they will feast .
http://gardening.about.com/od/gardenproblems/ig/Insects-and-Diseases-of-Plants/Wireworms.htm
Medical-Vocational Allowance for Herniated Disc
How to kill/control potato wireworms - Organic Gardening Forum ...
A friend has the wireworm problem in his garden. I was unable to find a pesticide that would eradicate them. IIRC, one extention service .
http://forums2.gardenweb.com/forums/load/organic/msg021806324734.html
Control Wireworms in Garden Vegetable Crops
image of gardening tips header . Wireworm larvae damage crops and plants by feeding on germinating seed, young seedlings, corms or tubers, and stunting or .
http://www.weekendgardener.net/garden-pests/wireworms-041004.htm
Appealing a Denial of Benefits
Wireworm Click Beetle.pub - Cornell University
The click beetle does not inflict damage; it is the wireworm that feeds on crops and . The most effective method of control for a home garden is the use of baits.
http://counties.cce.cornell.edu/oneida/home%20garden/INSECTS/Insects%20on%20Vegetables/Wireworm%20Click%20Beetle.pdf
home.


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public interface ItemPSVI
| Modifier and Type | Field and Description |
|---|---|
static short |
VALIDATION_FULL
Validation status indicating that full schema validation has been
performed on the item.
|
static short |
VALIDATION_NONE
Validation status indicating that schema validation has been performed
and the item in question has specifically been skipped.
|
static short |
VALIDATION_PARTIAL
Validation status indicating that schema validation has been performed
on the item in question under the rules of lax validation.
|
static short |
VALIDITY_INVALID
Validity value indicating that validation has been strictly assessed
and the item in question is invalid according to the rules of schema
validation.
|
static short |
VALIDITY_NOTKNOWN
Validity value indicating that validation has either not been performed
or that a strict assessment of validity could not be performed.
|
static short |
VALIDITY_VALID
Validation status indicating that schema validation has been performed
and the item in question is valid according to the rules of schema
validation.
|
| Modifier and Type | Method and Description |
|---|---|
ItemPSVI |
constant()
Returns a reference to an immutable instance with the same data
that this instance of
ItemPSVI currently has. |
Object |
getActualNormalizedValue()
Deprecated.
Use getSchemaValue().getActualValue() instead
|
short |
getActualNormalizedValueType()
Deprecated.
Use getSchemaValue().getActualValueType() instead
|
StringList |
getErrorCodes()
[schema error code] : a list of error codes generated from
the validation attempt or an empty StringList if no
errors occurred during the validation attempt. |
StringList |
getErrorMessages()
A list of error messages generated from the validation attempt or
an empty
StringList if no errors occurred during the
validation attempt. |
boolean |
getIsSchemaSpecified()
[schema specified] : if true, the value was specified in
the schema. |
ShortList |
getItemValueTypes()
Deprecated.
Use getSchemaValue().getListValueTypes() instead
|
XSSimpleTypeDefinition |
getMemberTypeDefinition()
[member type definition] : if and only if that type
definition is a simple type definition with {variety} union, or a
complex type definition whose {content type} is a simple type
definition with {variety} union, then an item isomorphic to that
member of the union's {member type definitions} which actually
validated the schema item's normalized value. |
String |
getSchemaDefault()
[schema default] : the canonical lexical representation of
the declaration's {value constraint} value. |
String |
getSchemaNormalizedValue()
Deprecated.
Use getSchemaValue().getNormalizedValue() instead
|
XSValue |
getSchemaValue()
If this item has a simple type definition or a complex type with simple
content, then return the value with respect to the simple type.
|
XSTypeDefinition |
getTypeDefinition()
[type definition] : an item isomorphic to the type
definition used to validate the schema item. |
short |
getValidationAttempted()
[validation attempted] : determines the extent to which
the schema item has been validated. |
String |
getValidationContext()
The nearest ancestor element information item with a
[schema information] property (or this element item
itself if it has such a property). |
short |
getValidity()
[validity] : determines the validity of the schema item
with respect to the validation being attempted. |
boolean |
isConstant()
Returns
true if this specific instance of
ItemPSVI is immutable, otherwise false . |
static final short VALIDITY_NOTKNOWN
static final short VALIDITY_INVALID
static final short VALIDITY_VALID
static final short VALIDATION_NONE
static final short VALIDATION_PARTIAL
static final short VALIDATION_FULL
ItemPSVI constant()
ItemPSVI
currently has.boolean isConstant()
true
if this specific instance of
ItemPSVI
is immutable, otherwise false
.String getValidationContext()
[schema information]
property (or this element item
itself if it has such a property). For more information refer to
element validation context and attribute validation context .short getValidity()
[validity]
: determines the validity of the schema item
with respect to the validation being attempted. The value will be one
of the constants: VALIDITY_NOTKNOWN
,
VALIDITY_INVALID
or VALIDITY_VALID
.short getValidationAttempted()
[validation attempted]
: determines the extent to which
the schema item has been validated. The value will be one of the
constants: VALIDATION_NONE
,
VALIDATION_PARTIAL
or VALIDATION_FULL
.StringList getErrorCodes()
[schema error code]
: a list of error codes generated from
the validation attempt or an empty StringList
if no
errors occurred during the validation attempt.StringList getErrorMessages()
StringList
if no errors occurred during the
validation attempt. The indices of error messages in this list are
aligned with those in the [schema error code]
list.String getSchemaNormalizedValue()
[schema normalized value]
: the normalized value of this
item after validation.Object getActualNormalizedValue() throws XSException
[schema normalized value]
: Binding specific actual value
or null
if the value is in error.XSException
- NOT_SUPPORTED_ERR: Raised if the implementation does not support this
method.short getActualNormalizedValueType() throws XSException
STRING_DT, SHORT_DT
. If the type definition of this
value is a list type definition, this method returns
LIST_DT
. If the type definition of this value is a list
type definition whose item type is a union type definition, this
method returns LISTOFUNION_DT
. To query the actual value
of the list or list of union type definitions use
itemValueTypes
. If the actualNormalizedValue
is null
, this method returns UNAVAILABLE_DT
.XSException
- NOT_SUPPORTED_ERR: Raised if the implementation does not support this
method.ShortList getItemValueTypes() throws XSException
actualNormalizedValueType
is LIST_DT
, the
returned array consists of one type kind which represents the itemType
. For example:
<simpleType name="listtype"> <list itemType="positiveInteger"/> </simpleType> <element name="list" type="listtype"/> ... <list>1 2 3</list>The
schemaNormalizedValue
value is "1 2 3", the
actualNormalizedValueType
value is LIST_DT
,
and the itemValueTypes
is an array of size 1 with the
value POSITIVEINTEGER_DT
.
LISTOFUNION_DT
,
for each actual value in the list the array contains the
corresponding memberType kind. For example:
<simpleType name='union_type' memberTypes="integer string"/> <simpleType name='listOfUnion'> <list itemType='union_type'/> </simpleType> <element name="list" type="listOfUnion"/> ... <list>1 2 foo</list>The
schemaNormalizedValue
value is "1 2 foo", the
actualNormalizedValueType
is LISTOFUNION_DT
, and the itemValueTypes
is an array of size 3 with the
following values: INTEGER_DT, INTEGER_DT, STRING_DT
.XSException
- NOT_SUPPORTED_ERR: Raised if the implementation does not support this
method.XSValue getSchemaValue()
XSTypeDefinition getTypeDefinition()
[type definition]
: an item isomorphic to the type
definition used to validate the schema item.XSSimpleTypeDefinition getMemberTypeDefinition()
[member type definition]
: if and only if that type
definition is a simple type definition with {variety} union, or a
complex type definition whose {content type} is a simple type
definition with {variety} union, then an item isomorphic to that
member of the union's {member type definitions} which actually
validated the schema item's normalized value.String getSchemaDefault()
[schema default]
: the canonical lexical representation of
the declaration's {value constraint} value. For more information
refer to element schema default and attribute schema default.boolean getIsSchemaSpecified()
[schema specified]
: if true, the value was specified in
the schema. If false, the value comes from the infoset. For more
information refer to element specified and attribute specified.Copyright © 2023. All rights reserved.


TickyBot
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TickyBot is a Slack Bot 🤖 that lets you use support tickets on
Slack. This way, you don't need to leave your beloved channel to
assist your users, if you don't feel like it.
With TickyBot you can easily check if you've received new tickets,
change their status, or write a reply, and the ticket will instantly
update itself on our site.
Support requests shouldn't be handled through Slack's direct messages.
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look into the issue.
How It Works
You can add TickyBot to your Slack workplace by scrolling down or clicking here
Your users can open a ticket by typing /ticket
and their message into the chat field
Once you or another admin solved their problems you can type /ticket close #id
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Active Job
Active Job objects can be configured to work with different backend queuing frameworks. To specify a queue adapter to use:
ActiveJob::Base.queue_adapter = :inline
A list of supported adapters can be found in QueueAdapters.
Active Job objects can be defined by creating a class that inherits from the ActiveJob::Base class. The only necessary method to implement is the “perform” method.
To define an Active Job object:
class ProcessPhotoJob < ActiveJob::Base
def perform(photo)
photo.watermark!('Rails')
photo.rotate!(90.degrees)
photo.resize_to_fit!(300, 300)
photo.upload!
end
end
Records that are passed in are serialized/deserialized using Global ID. More information can be found in Arguments.
To enqueue a job to be performed as soon the queueing system is free:
ProcessPhotoJob.perform_later(photo)
To enqueue a job to be processed at some point in the future:
ProcessPhotoJob.set(wait_until: Date.tomorrow.noon).perform_later(photo)
More information can be found in ActiveJob::Core::ClassMethods#set
A job can also be processed immediately without sending to the queue:
ProcessPhotoJob.perform_now(photo)
Exceptions
-
DeserializationError - Error class for deserialization errors.
-
SerializationError - Error class for serialization errors.


Do not go to places like a GP surgery, pharmacy or hospital if you think you might have coronavirus. Stay at home.
Get more advice about coronavirus in children.
Urgent advice: Use the 111 online coronavirus service if you have either:
- a high temperature – this means you feel hot to touch on your chest or back (you do not need to measure your temperature)
- a new, continuous cough – this means coughing a lot for more than an hour, or 3 or more coughing episodes in 24 hours (if you usually have a cough, it may be worse than usual)
These are the main symptoms of coronavirus.
The 111 online coronavirus service will ask about your symptoms and tell you what to do.
Only call 111 if you cannot get help online.
Babies and children
Call 111 if you're worried about a baby or child.
If they seem very unwell, are getting worse, or you think there's something seriously wrong, call 999.
Do not delay getting help if you're worried. Trust your instincts.
Get more advice about coronavirus in children.
Staying at home if you have symptoms (self-isolation)
If your symptoms are mild, you'll usually be advised to not leave your home for at least 7 days.
Anyone you live with should not leave your home for 14 days.
This is called self-isolation.
Find out more about self-isolation and treatment if you or someone you live with has symptoms.


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We equip your home or business's unused outdoor space (rooftops, yards, etc.) with hydroponic technology that allows it to grow plants without the use of traditional farming methods.
We remotely monitor the growth of your plants, and use machine learning algorithms to ensure that the plants recieve the exact nutrients that they need to thrive. The plants consume minimal water, and unlike traditional methods don't cause runoff pollution or result in deforrestation.
At the conclusion of a growing cycle, collect the grown plants from your building, and distribute them to local produce stores. Afterwards, we'll pay you for the plants that you've grown.
If you're Interested in starting the process of growing food, we'll conduct a survey of your buildings's growing spaces for free. In the survey, we'll determine the best crops to grow in your area, provide an estimated installation timeline, and calculate how much money you can expect to earn through your farm on a regular basis
After your building has been surveyed, we'll send a team to install a hydroponic system on your home. The installation is minimally invasive, and can be undone at any time. The designs are modular, aesthetically appealing, and can exist in a variety of building configurations - including on angled rooftops and on rooftops which already utilize solar panels. For this step, you can either pay up-front, or have the cost subtracted from your first few crop yeilds (for a net-zero out of pocket cost).
After the hydroponic system is installed, the growing process can begin. Right now, our systems are specialized to grow profitable crops such as strawberries and lettuce. Using internal monitoring systems, the hydronic system will ensure that the crops recieve enough water, sunlight, and nutrients, and will keep you informed of plant progress and problems through the app. Since most processes are automated, you won't need to do much at all.
Once your plants are finished growing, we'll collect them. Our team will aggregrate your plants with the plants of other nearby growers, and then sell them together to local produce stores. You'll then be delivered a majority of the profit from the sales throgh the app, and your system will be provided with new seeds. For plants like lettuce and strawberries, one growing cycle is approximately 30 days - meaning that you're set to earn money throughout the year.
Unlike traditional farms, we use precision technology to ensure that each plant only gets the water that it needs, and conserve the rest for other purposes.
Our product was galvanized when we saw current agricultural processes burning down the Amazon. Our process only utilizes existing land.
Unlike most farms, we do not create run-off pollution. In addition, we aspire to have all plastic systems come from reclaimed ocean plastic.
Environmental Impact is why we founded Aquaduct. Here are some advantages of our system, versus the current methods of factory farming.


mcsim()
simulates metacommunity dynamics in a given
landscape. Community dynamics are modeled based on the Beverton-Holt
function. Although this function is designed to be compatible with
brnet()
, users can provide their distance matrix to
simulate dynamics in any landscape. The key arguments are the number of
habitat patches (n_patch
) and the number of species in a
metacommunity (n_species
). Metacommunity dynamics are
simulated through (1) local dynamics (population growth and competition
among species), (2) immigration, and (3) emigration.
The function returns:
df_dynamics
data frame containing simulated
metacommunity dynamics*.
df_species
data frame containing species
attributes.
df_patch
data frame containing patch attributes.
df_diversity
data frame containing diversity metrics
(α, β, and γ).
distance_matrix
distance matrix used in the
simulation.
interaction_matrix
species interaction matrix, in
which species X (column) influences species Y (row).
*NOTE: The warm-up and burn-in periods will not be included in return values.
The following script simulates metacommunity dynamics with
n_patch = 5
and n_species = 5
. By default,
mcsim()
simulates metacommunity dynamics with 200 warm-up
(initialization with species introductions: n_warmup
), 200
burn-in (burn-in period with no species introductions:
n_burnin
), and 1000 time-steps for records
(n_timestep
).
mc <- mcsim(n_patch = 5, n_species = 5)
Users can visualize the simulated dynamics using
plot = TRUE
, which will show five sample patches and
species that are randomly chosen:
mc <- mcsim(n_patch = 5, n_species = 5, plot = TRUE)
A named list of return values:
mc
#> $df_dynamics
#> # A tibble: 25,000 × 9
#> timestep patch_id mean_env env carrying_capacity species niche_optim
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 0 0.0230 100 1 0.935
#> 2 1 1 0 0.0230 100 2 -0.575
#> 3 1 1 0 0.0230 100 3 0.223
#> 4 1 1 0 0.0230 100 4 -0.993
#> 5 1 1 0 0.0230 100 5 0.237
#> 6 1 2 0 -0.206 100 1 0.935
#> 7 1 2 0 -0.206 100 2 -0.575
#> 8 1 2 0 -0.206 100 3 0.223
#> 9 1 2 0 -0.206 100 4 -0.993
#> 10 1 2 0 -0.206 100 5 0.237
#> # ℹ 24,990 more rows
#> # ℹ 2 more variables: r_xt <dbl>, abundance <dbl>
#>
#> $df_species
#> # A tibble: 5 × 6
#> species mean_abundance r0 niche_optim sd_niche_width p_dispersal
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 4 0.935 0.628 0.1
#> 2 2 0 4 -0.575 0.200 0.1
#> 3 3 54.2 4 0.223 0.864 0.1
#> 4 4 0 4 -0.993 0.237 0.1
#> 5 5 67.2 4 0.237 0.615 0.1
#>
#> $df_patch
#> # A tibble: 5 × 5
#> patch_id alpha_div mean_env carrying_capacity disturbance
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 2 0 100 0
#> 2 2 2 0 100 0
#> 3 3 2 0 100 0
#> 4 4 2 0 100 0
#> 5 5 2 0 100 0
#>
#> $df_diversity
#> # A tibble: 1 × 3
#> alpha_div beta_div gamma_div
#> <dbl> <dbl> <dbl>
#> 1 2 1 2
#>
#> $df_xy_coord
#> # A tibble: 5 × 2
#> x_coord y_coord
#> <dbl> <dbl>
#> 1 8.65 4.74
#> 2 6.17 1.18
#> 3 9.83 2.88
#> 4 2.37 8.12
#> 5 1.82 4.19
#>
#> $distance_matrix
#> 1 2 3 4 5
#> 1 0.000000 4.338559 2.204784 7.136938 6.850778
#> 2 4.338559 0.000000 4.035728 7.913036 5.291787
#> 3 2.204784 4.035728 0.000000 9.122663 8.117620
#> 4 7.136938 7.913036 9.122663 0.000000 3.959281
#> 5 6.850778 5.291787 8.117620 3.959281 0.000000
#>
#> $interaction_matrix
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 1 0 0 0 0
#> [2,] 0 1 0 0 0
#> [3,] 0 0 1 0 0
#> [4,] 0 0 0 1 0
#> [5,] 0 0 0 0 1
brnet()
+ mcsim()
Return values of brnet()
are compatible with
mcsim()
. For example, df_patch$environment
,
df_patch$n_patch_upstream
, and
df_patch$distance_matrix
may be used to inform parameters
of mcsim()
:
Users may use the following arguments to custom metacommunity simulations regarding (1) species attributes, (2) competition, (3) patch attributes, and (4) landscape structure.
Arguments: r0
, niche_optim
OR min_optim
and max_optim
,
sd_niche_width
OR min_niche_width
and
max_niche_width
, niche_cost
,
p_dispersal
, zeta
Species attributes are determined based on the maximum reproductive
rate r0
, optimal environmental value
niche_optim
(or min_optim
and
max_optim
for random generation of
niche_optim
), niche width sd_niche_width
(or
min_niche_width
and max_niche_width
for random
generation of sd_niche_width
) and dispersal probability
p_dispersal
(see Model description for
details).
For optimal environmental values (niche optimum), the function by
default assigns random values to species as: \(\mu_i \sim Unif(\mu_{min}, \mu_{max})\),
where users can set values of \(\mu_{min}\) and \(\mu_{max}\) using min_optim
and max_optim
arguments (default:
min_optim = -1
and max_optim = 1
).
Alternatively, users may specify species niche optimums using the
argument niche_optim
(scalar or vector). If a single value
or a vector of niche_optim
is provided, the function
ignores min_optim
and max_optim
arguments.
Similarly, the function by default assigns random values of \(\sigma_{niche}\) to species as: \(\sigma_{niche,i} \sim Unif(\sigma_{niche,min},
\sigma_{niche,max})\). Users can set values of \(\sigma_{niche,min}\) and \(\sigma_{niche,max}\) using
min_niche_width
and max_niche_width
arguments
(default: min_niche_width = 0.1
and
max_niche_width = 1
). If a single value or a vector of
sd_niche_width
is provided, the function ignores
min_niche_width
and max_niche_width
arguments.
The argument niche_cost
determines the cost of having
wider niche. Smaller values imply greater costs of wider niche (i.e.,
decreased maximum reproductive rate; default:
niche_cost = 1
). To disable (no cost of wide niche), set
niche_cost = Inf
.
For other parameters, users may specify species attributes by giving
a scalar (assume identical among species) or a vector of values whose
length must be one or equal to n_species
. Default values
are r0 = 4
, sd_niche_width = 1
, and
p_dispersal = 0.1
.
The argument zeta
determines the sensitivity to
environmental pollutants as \(exp(-\zeta~impact)\), which will be
multiplied with the patch-specific population growth to represent
negative impacts of pollutants. \(impact\) represents the concentration of
hypothetical environmental pollutants (see q
in patch
attributes). The parameter \(\zeta\)
determines the sensitivity to environmental pollutants, and larger
values indicate greater species sensitivity. No pollutant effect if
\(\zeta = 0\).
Arguments: interaction_type
,
alpha
OR min_alpha
and
max_alpha
The argument interaction_type
determines whether
interaction coefficient alpha
is a constant or random
variable. If interaction_type = "constant"
, then the
interaction coefficients \(\alpha_{ij, i \ne
j}\) for any pairs of species will be set as a constant
alpha
(i.e., off-diagonal elements of the interaction
matrix). If interaction_type = "random"
, \(\alpha_{ij, i \ne j}\) will be drawn from a
uniform distribution as \(\alpha_{ij, i \ne j}
\sim Unif(\alpha_{min}, \alpha_{max})\) with corresponding
arguments min_alpha
and max_alpha
. The
argument alpha
is ignored under the scenario of random
interaction strength (i.e., interaction_type = "random"
).
Note that the diagonal elements of the interaction matrix (\(\alpha_{ii}\)) are always 1.0 regardless of
interaction_type
, as alpha
is the strength of
interspecific competition relative to that of intraspecific competition
(see Model description). By default,
interaction_type = "constant"
and
alpha = 0
.
Arguments: carrying_capacity
,
mean_env
, sd_env
,
spatial_env_cor
, phi
, p_disturb
,
i_disturb
, impact
The arguments carrying_capacity
(default:
carrying_capacity = 100
) and mean_env
(default: mean_env = 0
) determines mean environmental
values of habitat patches, which can be a scalar (assume identical among
patches) or a vector (length must be equal to n_patch
).
The arguments sd_env
(default:
sd_env = 0.1
), spatial_env_cor
(default:
spatial_env_cor = FALSE
) and phi
(default:
phi = 1
) determine spatio-temporal dynamics of
environmental values. sd_env
determines the magnitude of
temporal environmental fluctuations. If
spatial_env_cor = TRUE
, the function models spatial
autocorrelation of temporal environmental fluctuation based on a
multi-variate normal distribution. The degree of spatial autocorrelation
would be determined by phi
, the parameter describing the
strength of distance decay in spatial autocorrelation.
Users can also define disturbance probability
(p_disturb
) and mortality (i_disturb
). When
disturbance occurs, all habitat patches are reduced by
i_disturb
. i_disturb
can be identical or
different across habitat patches.
Lastly, the argument impact
defines concentration of
hypothetical environmental pollutants. The values will be converted as
\(exp(-\zeta~impact)\), which will be
multiplied with the patch-specific population growth. Thus, a greater
reduction factor will be applied as the value of \(impact\) increases. The parameter \(\zeta\) determines the sensitivity to
environmental pollutants (see argument zeta
in species
attributes).
Arguments: xy_coord
OR
distance_matrix
, landscape_size
,
theta
These arguments define landscape structure. By default, the function
produces a square-shaped landscape (landscape_size = 10
on
a side) in which habitat patches are distributed randomly through a
Poisson point process (i.e., x- and y-coordinates of patches are drawn
from a uniform distribution). The parameter θ describes the shape of
distance decay in species dispersal (see Model
description) and determines patches’ structural connectivity
(default: theta = 1
). Users can define their landscape by
providing either xy_coord
or distance_matrix
(landscape_size
will be ignored if either of these
arguments is provided). If xy_coord
is provided (2-column
data frame denoting x- and y-coordinates of patches, respectively;
NULL
by default), the function calculates the distance
between patches based on coordinates. Alternatively, users may provide
distance_matrix
(the object must be matrix
),
which describes the distance between habitat patches. The argument
distance_matrix
overrides xy_coord
.
Arguments: n_warmup
,
n_burnin
, n_timestep
The argument n_warmup
is the period during which species
introductions occur (default: n_warmup = 200
). The initial
number of individuals introduced follows a Poisson distribution with a
mean of 0.5 and independent across space and time. This random
introduction events occur multiple times over the n_warmup
period, in which propagule_interval
determines the timestep
interval of the random introductions (default:
propagule_interval = ceiling(n_warmup / 10)
).
The argument n_burnin
is the period that will be
discarded as burn-in to remove the influence of initial values
(default: n_burnin = 200
). During the burn-in period,
species introductions do not occur.
The argument n_timestep
is the simulation peiord that is
recorded in the return df_dynamics
(default:
n_timestep = 1000
). As a result, with the default setting,
the function simulates 1400 timesteps (n_warmup
+
n_burnin
+ n_timestep
= 1400) but returns only
the last 1000 timesteps as the resulting metacommunity dynamics. All the
derived statistics (e.g., diversity metrics in df_diversity
and df_patch
) will be calculated based on the results
during n_timestep
.
Full model descriptions are available at: https://doi.org/10.1073/pnas.2105574118


Guidance
Guide for local authorities completing the school workforce census 2014.
Ref: DFE-00577-2014 PDF , 1.69MB , 68 pages
This guide provides school workforce census 2014 preparation instructions for children’s services staff in local authorities so that they:
Published: 9 September 2014
Updated: 11 November 2014


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{% extends 'login/subpages_base.html' %} {% load staticfiles %} {% block body %}
Your password has been set. You may go ahead and login now.


All modulz are built movile-first for maximum
device compatibility
Support for IE8,mobile and tablet devices
screenreaders and color blind
On different screen sizes,fonts automagically
scale with the viewport
Make any design your own using the Style
Editor.Personalize fonts.colors.and layouts to
create the custom look you want.
China will come up with its own smart car
standards.traffic rules,laws and regulations
relating to safety.according to the China
Industry Innovation Alliance for the Intelli-
gent and Connected Vehicles.
Li Jun,a China Academy of Engi-
neering academician and also
director of the alliance's experts'
committee,said that China will
also launch design guidance for
smart cars,smart map architec-
ture and standards for test
grounds
Li Jun,a China Academy of Engi-
neering academician and also
director of the alliance's experts'
committee,said that China will
also launch design guidance for
smart cars,smart map architec-
ture and standards for test
grounds
Li Jun,a China Academy of Engi-
neering academician and also
director of the alliance's experts'
committee,said that China will
also launch design guidance for
smart cars,smart map architec-
ture and standards for test
grounds
Li Jun,a China Academy of Engineering
academician and also director of the
alliance's committee,said that
China will also launch design guidance
for smart cars,smart map architecture
and standards for test grounds.
China will come up with its own smart
car standards,traffic rules,laws and reg-
ulations relating to safety.according to
the China Industry Innovation Alliance
for the Intelligent and Connected
Vehicles.
Our unique online teaching style makes learning easy for everyone.Whether you
are trying to land a new job.brush up on your skills
Make any design your owb using the Style
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Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 4
A great mid-week treat - this curry can be made as mild or spicy as you like. Serve with rice or naan breads.
This meal provides 323 kcal, 38g protein, 6.5g carbohydrate (of which 5g sugars), 16g fat (of which 8g saturates), 3.5g fibre and 0.7g salt per portion.
Cut the chicken into bite-size pieces and mix with the lemon juice, and garam masala and season with salt.
Heat the oil or ghee in a deep frying pan or saucepan over a medium heat and cook the onion for 6-7 minutes until softened and becoming golden. Add the chicken and fry for 3-4 minutes until it has become opaque in colour. Stir in the Madras paste and cook for a couple of minutes before adding the chopped tomatoes and coconut. Cover with a lid and leave to simmer gently for about 20 minutes. Stir in the chopped coriander and serve straight away with naan bread.


1 write to include
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Microsoft.PowerShell.Commands.Management (3)
commands\management\Service.cs (3)
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Preparation time
less than 30 mins
Cooking time
1 to 2 hours
Serves
Serves 6-8
Dietary
Invented by two elderly spinsters, the Tatin sisters, this sensuous dessert is one of Raymond Blanc’s favourites. Combining the pleasures of dark caramel, crisp pastry and the sweet acidity of apples, it deserves to be served with the very best crème fraîche (“full fat, please,” says Raymond) or vanilla ice cream.
Equipment and preparation: You will need a 20cm/8in tarte Tatin dish.
Preheat the oven to 180C/350F/Gas 4.
Cut the apples in half horizontally, remove the cores and slice off the rounded tops and bottoms of the apples so they sit flat in the tin.
Put the water in the bottom of the tin and sprinkle over the sugar. Let it sit for two minutes to allow the water to absorb the sugar.
On a medium heat, cook the syrup until it turns to a pale blonde caramel (do not let it become too dark or the flavour will overpower the apples), then stir in the diced butter - this will stop the caramel cooking and give extra richness to the dish.
Arrange the apples, middles uppermost, around the edge of the tin and then fill in the centre with the remaining apples. It is important to pack them as tightly as possible – press them down with your hands as you go.
Brush the apples with the melted butter and place the tin on a baking tray. Bake in the oven for 30 minutes.
Remove from the oven and place the disc of frozen pastry on the top. Tuck the edges of the pastry into the dish and prick a few holes with a sharp knife to let the steam out as the dish cooks.
Return to the oven for a further 40-45 minutes, or until the pastry is crisp and golden-brown.
Allow to cool at room temperature for one hour before turning out of the tin and serving warm. Alternatively, make the tarte the day before and reheat as above.


Notes: 1. Ranking or % for every actor is calculated by setting the actor with the highest value on each category to 100% and then rank the remaining actors accordingly
2. Bargaining activity is the total number of bargains by an actor


{% extends "shorturl/base.html" %} {% load staticfiles %} {% block title %}URL Shortner - Make your URL shorter!{% endblock %} {% block content %}
Cut your links, into MUCH shorter ones, Just one click to go..!!
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Nightwish Ipsum
Write the lyrics for a song
even you
won't understand!
How many paragraphs?
How many sentences in a paragraph, on average?
Generate!


Installation¶
No installation is required. Just clone this package anywhere onto your disk and you are ready to start.
git clone <URL>
However, this package has several dependencies that have to be fulfilled. It is recommended to use a dedicated anaconda
environment with python3
. You create it using
conda create -n ionsrcopt python=3
and whenever you want to use it you have to activate it with
conda activate ionsrcopt
The dependencies you have to install (with conda install
or with pip install
if the package cannot be installed with conda) into this environment, i.e. you have to activate it first, are
numpy
pandas
scipy
matplotlib
statsmodels
jupyter (if you want to use any of the jupyter notebooks)
sphinx and sphinx_rtd_theme (for building the documentation)
Setup¶
We will need various folders so it is best to create them now.
Under the root create three folders called Data_Raw, Data_Preprocessed and Data_Clustered. Then, in the visualization directory create a folder Results. To avoid later troubles, please use the same capitalization. The first three folders will store the data during the steps of our analysis, and in the last folder the summarized cluster results will be saved.
To build the documentation as html files, run make html
from inside the docs directory. Please make sure you have sphinx
and sphinx_rtd_theme
installed.


Treasury Secretary Steven Mnuchin gives a briefing to discuss regulatory issues associated with cryptocurrency. FOX Business Network (FBN) is a financial new...
lol it would be a good idea to see what the us is doing about crypto haha
hyperDEX15-Jul-19 11:13 PM
Trying to shut it down obviously, they just cannot get in touch with Satoshi @Cypher(edited)
Alecandro15-Jul-19 11:14 PM
Rebrand announcements soon??
haha
actionslacks15-Jul-19 11:14 PM
if satoshi is ever revealed he will get the assange treatment x10
hyperDEX15-Jul-19 11:15 PM
I say we tell trump via twitter it was craig
Now that would be funny
Craig be like - no i am not satoshi.....LOL
2
Blake16-Jul-19 12:00 AM
Can't be long now!
@hyperDEX is everything still planned for release today?
hyperDEX16-Jul-19 12:09 AM
Ye s final stretches
Alecandro16-Jul-19 12:10 AM
hyperDEX16-Jul-19 12:10 AM
I am just looking in, not part of the people actually doing work
Blake16-Jul-19 12:11 AM
Kmd is a good buy rn probably
hyperDEX16-Jul-19 12:11 AM
Well ICO price...

Alecandro16-Jul-19 12:56 AM
Lol
Kids these days...
yorklab16-Jul-19 12:56 AM
This is just the start.
Alecandro16-Jul-19 12:57 AM
Haha everyone who bought hoping for a rebrand pump all sour now
yorklab16-Jul-19 12:57 AM
yes
manouchkov7116-Jul-19 01:06 AM
Helo
Alecandro16-Jul-19 01:06 AM
DingDongSoLong16-Jul-19 01:07 AM
All the mad people are just day traders looking to make a quick buck
Alecandro16-Jul-19 01:07 AM
Yep pretty much
Yassin16-Jul-19 01:08 AM
welcome @manouchkov71
ComputerGenie 👴👴👴16-Jul-19 01:08 AM
not all of them
Draeth16-Jul-19 01:08 AM
i feel like my kmd coin had an influence on the new logo
2
manouchkov7116-Jul-19 01:09 AM
Thank you @Yassin
ComputerGenie 👴👴👴16-Jul-19 01:09 AM
@Draeth I'm sending mine back, I want an upgrade
Draeth16-Jul-19 01:09 AM
loll
dogracer16-Jul-19 01:11 AM
New logo is sweet, Antara as a brand comes off pretty polished as well. I got some more learnin to do it seems. exciting times KMD community!
Draeth16-Jul-19 01:11 AM
time to get to work on a new version of the KMD coin
1
Yassin16-Jul-19 01:12 AM
np @manouchkov71 your are welcome(edited)
Portman16-Jul-19 01:47 AM
New website
1. Content is good!
2. The typeface, kerning and 'm' in the new logo are off.
3. Dropdown menu should happen on mouseover, not by having the user click to access the dropdown menu and then another click to access the submenu item.
When you click to open the dropdown menu without clicking a submenu option and scroll the page down, that dropdown remains open and blocks content.
The submenu items do not need a horizontal line spacer, white space is best, people will not get confused if hr is removed. Remove mouseover text underlining as well. This is all just lazy megamenu editing by the devs that should have been changed from the start.
4. Remove drop shadows on everything. It is 2001 fad, not a clean look.
5. Buttons: Change the button look and animation. The button mouseovers moving the text up and underlining the word and jittering back to default position is bad. Kill all dropshadows, text underlines and button borders.
Keep the buttons simple. On mouseover have the button background color change and that is all. There is a lack on consistency as well with all the buttons - footer, 'view all news' buttons respond differently.
For text links, just change the font color on mouseover, do not underline the text link.
5
6. The infinite scroll on the news page will not be processed by Googlebots/search crawlers. They cannot not access all individual items in the feed. If crawlers cannot access your content, it is unlikely to surface in search results. SEO will fail there.
7. The Antara animation on the home page is good. Since the rest of the site has fluid edge to edge backgrounds, that section should also have a edge to edge background tying in with the color on the block animation to keep the flow. If at all possible, remove the fade to black transitions and just fade in and out the animations sequences on each other. Also remove the outer dropshadow.
Although the content is organized well, the site itself appears like a plug and play wordpress theme, a megamenu with no tweaking, and general css changes. Designers did not make this site, wordpress hackers did.
2
1
hyperDEX16-Jul-19 02:26 AM
@Portman great feedback, i can see that the team has been made aware of it.
!kmdtip @Portman 1
KMD TipBotBOT16-Jul-19 02:27 AM
TipBot
Cannot tip with entered amount.
hyperDEX16-Jul-19 02:27 AM
lol
Will tip you something when i deposit some KMD
joe2x416-Jul-19 03:05 AM
!arrrtip @Portman 5
ARRR TipBotBOT16-Jul-19 03:05 AM
TipBot
@joe2x4 (joe2x4#5987)
You tipped @Portman4.9999 ARRR
A network fee of 0.0001 ARRR was deducted from the amount.
This operation is pending. You will be notified when when it has finished.
epitome16-Jul-19 03:31 AM
Yes I'm having the Ledger sync issue too...
Will the ledger sync issue impact on the ability to claim block rewards through the website?
I'll test it out
meritfans16-Jul-19 03:33 AM
like the old logo
epitome16-Jul-19 03:36 AM
It worked fine!
dumpling16-Jul-19 04:00 AM
yeah,the old logo is much better,I think
meritfans16-Jul-19 04:07 AM
Yes, I don’t think the new brand is recognizable.
too many logo is circle
nymkappa16-Jul-19 04:10 AM
I posting in marketing channel but I am not sure this is the right place... I want to report some issues with the website
Where should I post? There are so many channels hahaha


Data Set Characteristics: |
Multivariate |
Number of Instances: |
699 |
Area: |
Life |
Attribute Characteristics: |
Integer |
Number of Attributes: |
10 |
Date Donated |
1992-07-15 |
Associated Tasks: |
Classification |
Missing Values? |
Yes |
Number of Web Hits: |
128481 |
Source:
Creator:
Dr. WIlliam H. Wolberg (physician)
University of Wisconsin Hospitals
Madison, Wisconsin, USA
Donor:
Olvi Mangasarian (mangasarian '@' cs.wisc.edu)
Received by David W. Aha (aha '@' cs.jhu.edu)
Data Set Information:
Samples arrive periodically as Dr. Wolberg reports his clinical cases. The database therefore reflects this chronological grouping of the data. This grouping information appears immediately below, having been removed from the data itself:
Group 1: 367 instances (January 1989)
Group 2: 70 instances (October 1989)
Group 3: 31 instances (February 1990)
Group 4: 17 instances (April 1990)
Group 5: 48 instances (August 1990)
Group 6: 49 instances (Updated January 1991)
Group 7: 31 instances (June 1991)
Group 8: 86 instances (November 1991)
-----------------------------------------
Total: 699 points (as of the donated datbase on 15 July 1992)
Note that the results summarized above in Past Usage refer to a dataset of size 369, while Group 1 has only 367 instances. This is because it originally contained 369 instances; 2 were removed. The following statements summarizes changes to the original Group 1's set of data:
##### Group 1 : 367 points: 200B 167M (January 1989)
##### Revised Jan 10, 1991: Replaced zero bare nuclei in 1080185 & 1187805
##### Revised Nov 22,1991: Removed 765878,4,5,9,7,10,10,10,3,8,1 no record
##### : Removed 484201,2,7,8,8,4,3,10,3,4,1 zero epithelial
##### : Changed 0 to 1 in field 6 of sample 1219406
##### : Changed 0 to 1 in field 8 of following sample:
##### : 1182404,2,3,1,1,1,2,0,1,1,1
Attribute Information:
1. Sample code number: id number
2. Clump Thickness: 1 - 10
3. Uniformity of Cell Size: 1 - 10
4. Uniformity of Cell Shape: 1 - 10
5. Marginal Adhesion: 1 - 10
6. Single Epithelial Cell Size: 1 - 10
7. Bare Nuclei: 1 - 10
8. Bland Chromatin: 1 - 10
9. Normal Nucleoli: 1 - 10
10. Mitoses: 1 - 10
11. Class: (2 for benign, 4 for malignant)
Relevant Papers:
Wolberg, W.H., & Mangasarian, O.L. (1990). Multisurface method of pattern separation for medical diagnosis applied to breast cytology. In Proceedings of the National Academy of Sciences, 87, 9193--9196.
[Web Link]
Zhang, J. (1992). Selecting typical instances in instance-based learning. In Proceedings of the Ninth International Machine Learning Conference (pp. 470--479). Aberdeen, Scotland: Morgan Kaufmann.
[Web Link]
Papers That Cite This Data Set1:
Gavin Brown. Diversity in Neural Network Ensembles. The University of Birmingham. 2004. [View Context].
Krzysztof Grabczewski and Wl/odzisl/aw Duch. Heterogeneous Forests of Decision Trees. ICANN. 2002. [View Context].
András Antos and Balázs Kégl and Tamás Linder and Gábor Lugosi. Data-dependent margin-based generalization bounds for classification. Journal of Machine Learning Research, 3. 2002. [View Context].
Kristin P. Bennett and Ayhan Demiriz and Richard Maclin. Exploiting unlabeled data in ensemble methods. KDD. 2002. [View Context].
Hussein A. Abbass. An evolutionary artificial neural networks approach for breast cancer diagnosis. Artificial Intelligence in Medicine, 25. 2002. [View Context].
Baback Moghaddam and Gregory Shakhnarovich. Boosted Dyadic Kernel Discriminants. NIPS. 2002. [View Context].
Robert Burbidge and Matthew Trotter and Bernard F. Buxton and Sean B. Holden. STAR - Sparsity through Automated Rejection. IWANN (1). 2001. [View Context].
Nikunj C. Oza and Stuart J. Russell. Experimental comparisons of online and batch versions of bagging and boosting. KDD. 2001. [View Context].
Yuh-Jeng Lee. Smooth Support Vector Machines. Preliminary Thesis Proposal Computer Sciences Department University of Wisconsin. 2000. [View Context].
Justin Bradley and Kristin P. Bennett and Bennett A. Demiriz. Constrained K-Means Clustering. Microsoft Research Dept. of Mathematical Sciences One Microsoft Way Dept. of Decision Sciences and Eng. Sys. 2000. [View Context].
Lorne Mason and Peter L. Bartlett and Jonathan Baxter. Improved Generalization Through Explicit Optimization of Margins. Machine Learning, 38. 2000. [View Context].
P. S and Bradley K. P and Bennett A. Demiriz. Constrained K-Means Clustering. Microsoft Research Dept. of Mathematical Sciences One Microsoft Way Dept. of Decision Sciences and Eng. Sys. 2000. [View Context].
Endre Boros and Peter Hammer and Toshihide Ibaraki and Alexander Kogan and Eddy Mayoraz and Ilya B. Muchnik. An Implementation of Logical Analysis of Data. IEEE Trans. Knowl. Data Eng, 12. 2000. [View Context].
Chun-Nan Hsu and Hilmar Schuschel and Ya-Ting Yang. The ANNIGMA-Wrapper Approach to Neural Nets Feature Selection for Knowledge Discovery and Data Mining. Institute of Information Science. 1999. [View Context].
Huan Liu and Hiroshi Motoda and Manoranjan Dash. A Monotonic Measure for Optimal Feature Selection. ECML. 1998. [View Context].
Lorne Mason and Peter L. Bartlett and Jonathan Baxter. Direct Optimization of Margins Improves Generalization in Combined Classifiers. NIPS. 1998. [View Context].
W. Nick Street. A Neural Network Model for Prognostic Prediction. ICML. 1998. [View Context].
Yk Huhtala and Juha Kärkkäinen and Pasi Porkka and Hannu Toivonen. Efficient Discovery of Functional and Approximate Dependencies Using Partitions. ICDE. 1998. [View Context].
Kristin P. Bennett and Erin J. Bredensteiner. A Parametric Optimization Method for Machine Learning. INFORMS Journal on Computing, 9. 1997. [View Context].
Rudy Setiono and Huan Liu. NeuroLinear: From neural networks to oblique decision rules. Neurocomputing, 17. 1997. [View Context].
. Prototype Selection for Composite Nearest Neighbor Classifiers. Department of Computer Science University of Massachusetts. 1997. [View Context].
Erin J. Bredensteiner and Kristin P. Bennett. Feature Minimization within Decision Trees. National Science Foundation. 1996. [View Context].
Ismail Taha and Joydeep Ghosh. Characterization of the Wisconsin Breast cancer Database Using a Hybrid Symbolic-Connectionist System. Proceedings of ANNIE. 1996. [View Context].
Jennifer A. Blue and Kristin P. Bennett. Hybrid Extreme Point Tabu Search. Department of Mathematical Sciences Rensselaer Polytechnic Institute. 1996. [View Context].
Geoffrey I. Webb. OPUS: An Efficient Admissible Algorithm for Unordered Search. J. Artif. Intell. Res. (JAIR, 3. 1995. [View Context].
Rudy Setiono and Huan Liu. Neural-Network Feature Selector. Department of Information Systems and Computer Science National University of Singapore. [View Context].
Huan Liu. A Family of Efficient Rule Generators. Department of Information Systems and Computer Science National University of Singapore. [View Context].
Rudy Setiono. Extracting M-of-N Rules from Trained Neural Networks. School of Computing National University of Singapore. [View Context].
Jarkko Salojarvi and Samuel Kaski and Janne Sinkkonen. Discriminative clustering in Fisher metrics. Neural Networks Research Centre Helsinki University of Technology. [View Context].
Wl odzisl and Rafal Adamczak and Krzysztof Grabczewski and Grzegorz Zal. A hybrid method for extraction of logical rules from data. Department of Computer Methods, Nicholas Copernicus University. [View Context].
Charles Campbell and Nello Cristianini. Simple Learning Algorithms for Training Support Vector Machines. Dept. of Engineering Mathematics. [View Context].
Chotirat Ann and Dimitrios Gunopulos. Scaling up the Naive Bayesian Classifier: Using Decision Trees for Feature Selection. Computer Science Department University of California. [View Context].
Wl odzisl/aw Duch and Rudy Setiono and Jacek M. Zurada. Computational intelligence methods for rule-based data understanding. [View Context].
Rafael S. Parpinelli and Heitor S. Lopes and Alex Alves Freitas. An Ant Colony Based System for Data Mining: Applications to Medical Data. CEFET-PR, CPGEI Av. Sete de Setembro, 3165. [View Context].
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Rafael S. Parpinelli and Heitor S. Lopes and Alex Alves Freitas. PART FOUR: ANT COLONY OPTIMIZATION AND IMMUNE SYSTEMS Chapter X An Ant Colony Algorithm for Classification Rule Discovery. CEFET-PR, Curitiba. [View Context].
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Andrew I. Schein and Lyle H. Ungar. A-Optimality for Active Learning of Logistic Regression Classifiers. Department of Computer and Information Science Levine Hall. [View Context].
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Citation Request:
This breast cancer databases was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. If you publish results when using this database, then please include this information in your acknowledgements. Also, please cite one or more of:
1. O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18.
2. William H. Wolberg and O.L. Mangasarian: "Multisurface method of pattern separation for medical diagnosis applied to breast cytology", Proceedings of the National Academy of Sciences, U.S.A., Volume 87, December 1990, pp 9193-9196.
3. O. L. Mangasarian, R. Setiono, and W.H. Wolberg: "Pattern recognition via linear programming: Theory and application to medical diagnosis", in: "Large-scale numerical optimization", Thomas F. Coleman and Yuying Li, editors, SIAM Publications, Philadelphia 1990, pp 22-30.
4. K. P. Bennett & O. L. Mangasarian: "Robust linear programming discrimination of two linearly inseparable sets", Optimization Methods and Software 1, 1992, 23-34 (Gordon & Breach Science Publishers).
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