Class: Aws::Batch::Types::ComputeResource
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb |
Overview
An object that represents an ::Aws::Batch
compute resource. For more
information, see Compute environments in the Batch User Guide.
Constant Summary
Instance Attribute Summary
-
#allocation_strategy ⇒ String
rw
The allocation strategy to use for the compute resource if not enough instances of the best fitting instance type can be allocated.
-
#bid_percentage ⇒ Integer
rw
The maximum percentage that a Spot Instance price can be when compared with the On-Demand price for that instance type before instances are launched.
-
#desiredv_cpus ⇒ Integer
rw
The desired number of vCPUS in the compute environment.
-
#ec2_configuration ⇒ Array<Types::Ec2Configuration>
rw
Provides information that's used to select Amazon Machine Images (AMIs) for
::Aws::EC2
instances in the compute environment. -
#ec2_key_pair ⇒ String
rw
The Amazon
::Aws::EC2
key pair that's used for instances launched in the compute environment. -
#image_id ⇒ String
rw
The Amazon Machine Image (AMI) ID used for instances launched in the compute environment.
-
#instance_role ⇒ String
rw
The Amazon
::Aws::ECS
instance profile applied to Amazon EC2 instances in a compute environment. -
#instance_types ⇒ Array<String>
rw
The instances types that can be launched.
-
#launch_template ⇒ Types::LaunchTemplateSpecification
rw
The launch template to use for your compute resources.
-
#maxv_cpus ⇒ Integer
rw
The maximum number of vCPUs that a compute environment can support.
-
#minv_cpus ⇒ Integer
rw
The minimum number of vCPUs that a compute environment should maintain (even if the compute environment is
DISABLED
). -
#placement_group ⇒ String
rw
The Amazon
::Aws::EC2
placement group to associate with your compute resources. -
#security_group_ids ⇒ Array<String>
rw
The Amazon
::Aws::EC2
security groups that are associated with instances launched in the compute environment. -
#spot_iam_fleet_role ⇒ String
rw
The Amazon
::Aws::Batch::Resource
Name (ARN) of the Amazon EC2 Spot Fleet::Aws::IAM
role applied to aSPOT
compute environment. -
#subnets ⇒ Array<String>
rw
The VPC subnets where the compute resources are launched.
-
#tags ⇒ Hash<String,String>
rw
Key-value pair tags to be applied to
::Aws::EC2
resources that are launched in the compute environment. -
#type ⇒ String
rw
The type of compute environment:
EC2
,SPOT
,FARGATE
, orFARGATE_SPOT
.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#allocation_strategy ⇒ String
(rw)
The allocation strategy to use for the compute resource if not
enough instances of the best fitting instance type can be allocated.
This might be because of availability of the instance type in the
Region or Amazon ::Aws::EC2
service limits. For more information, see
Allocation strategies in the Batch User Guide.
BEST_FIT (default)
: ::Aws::Batch
selects an instance type that best fits the needs of the
jobs with a preference for the lowest-cost instance type. If
additional instances of the selected instance type aren't
available, Batch waits for the additional instances to be
available. If there aren't enough instances available or the user
is reaching Amazon EC2 service limits, additional jobs
aren't run until the currently running jobs are completed. This
allocation strategy keeps costs lower but can limit scaling. If
you're using Spot Fleets with BEST_FIT
, the Spot Fleet IAM Role
must be specified. Compute resources that use a BEST_FIT
allocation strategy don't support infrastructure updates and
can't update some parameters. For more information, see Updating
compute environments in the Batch User Guide.
BEST_FIT_PROGRESSIVE
: ::Aws::Batch
selects additional instance types that are large enough to
meet the requirements of the jobs in the queue. Its preference is
for instance types with lower cost vCPUs. If additional instances
of the previously selected instance types aren't available, Batch
selects new instance types.
SPOT_CAPACITY_OPTIMIZED
: ::Aws::Batch
selects one or more instance types that are large enough to
meet the requirements of the jobs in the queue. Its preference is
for instance types that are less likely to be interrupted. This
allocation strategy is only available for Spot Instance compute
resources.
SPOT_PRICE_CAPACITY_OPTIMIZED
: The price and capacity optimized allocation strategy looks at both price and capacity to select the Spot Instance pools that are the least likely to be interrupted and have the lowest possible price. This allocation strategy is only available for Spot Instance compute resources.
With BEST_FIT_PROGRESSIVE
,SPOT_CAPACITY_OPTIMIZED
and
SPOT_PRICE_CAPACITY_OPTIMIZED
strategies using On-Demand or Spot
Instances, and the BEST_FIT
strategy using Spot Instances, ::Aws::Batch
might need to exceed maxvCpus
to meet your capacity requirements.
In this event, ::Aws::Batch
never exceeds maxvCpus
by more than a single
instance.
# File 'gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb', line 744
class ComputeResource < Struct.new( :type, :allocation_strategy, :minv_cpus, :maxv_cpus, :desiredv_cpus, :instance_types, :image_id, :subnets, :security_group_ids, :ec2_key_pair, :instance_role, :, :placement_group, :bid_percentage, :spot_iam_fleet_role, :launch_template, :ec2_configuration) SENSITIVE = [] include Aws::Structure end
#bid_percentage ⇒ Integer
(rw)
The maximum percentage that a Spot Instance price can be when compared with the On-Demand price for that instance type before instances are launched. For example, if your maximum percentage is 20%, then the Spot price must be less than 20% of the current On-Demand price for that Amazon EC2 instance. You always pay the lowest (market) price and never more than your maximum percentage. If you leave this field empty, the default value is 100% of the On-Demand price. For most use cases, we recommend leaving this field empty.
# File 'gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb', line 744
class ComputeResource < Struct.new( :type, :allocation_strategy, :minv_cpus, :maxv_cpus, :desiredv_cpus, :instance_types, :image_id, :subnets, :security_group_ids, :ec2_key_pair, :instance_role, :, :placement_group, :bid_percentage, :spot_iam_fleet_role, :launch_template, :ec2_configuration) SENSITIVE = [] include Aws::Structure end
#desiredv_cpus ⇒ Integer
(rw)
The desired number of vCPUS in the compute environment. ::Aws::Batch
modifies this value between the minimum and maximum values based on
job queue demand.
# File 'gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb', line 744
class ComputeResource < Struct.new( :type, :allocation_strategy, :minv_cpus, :maxv_cpus, :desiredv_cpus, :instance_types, :image_id, :subnets, :security_group_ids, :ec2_key_pair, :instance_role, :, :placement_group, :bid_percentage, :spot_iam_fleet_role, :launch_template, :ec2_configuration) SENSITIVE = [] include Aws::Structure end
#ec2_configuration ⇒ Array
<Types::Ec2Configuration> (rw)
Provides information that's used to select Amazon Machine Images
(AMIs) for ::Aws::EC2
instances in the compute environment. If
Ec2Configuration
isn't specified, the default is ECS_AL2
.
One or two values can be provided.
# File 'gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb', line 744
class ComputeResource < Struct.new( :type, :allocation_strategy, :minv_cpus, :maxv_cpus, :desiredv_cpus, :instance_types, :image_id, :subnets, :security_group_ids, :ec2_key_pair, :instance_role, :, :placement_group, :bid_percentage, :spot_iam_fleet_role, :launch_template, :ec2_configuration) SENSITIVE = [] include Aws::Structure end
#ec2_key_pair ⇒ String
(rw)
The Amazon ::Aws::EC2
key pair that's used for instances launched in the
compute environment. You can use this key pair to log in to your
instances with SSH.
# File 'gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb', line 744
class ComputeResource < Struct.new( :type, :allocation_strategy, :minv_cpus, :maxv_cpus, :desiredv_cpus, :instance_types, :image_id, :subnets, :security_group_ids, :ec2_key_pair, :instance_role, :, :placement_group, :bid_percentage, :spot_iam_fleet_role, :launch_template, :ec2_configuration) SENSITIVE = [] include Aws::Structure end
#image_id ⇒ String
(rw)
The Amazon Machine Image (AMI) ID used for instances launched in the
compute environment. This parameter is overridden by the
imageIdOverride
member of the Ec2Configuration
structure.
# File 'gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb', line 744
class ComputeResource < Struct.new( :type, :allocation_strategy, :minv_cpus, :maxv_cpus, :desiredv_cpus, :instance_types, :image_id, :subnets, :security_group_ids, :ec2_key_pair, :instance_role, :, :placement_group, :bid_percentage, :spot_iam_fleet_role, :launch_template, :ec2_configuration) SENSITIVE = [] include Aws::Structure end
#instance_role ⇒ String
(rw)
The Amazon ::Aws::ECS
instance profile applied to Amazon EC2 instances in a
compute environment. You can specify the short name or full Amazon
::Aws::Batch::Resource
Name (ARN) of an instance profile. For example,
ecsInstanceRole
or
arn:aws:iam::<aws_account_id>:instance-profile/ecsInstanceRole
.
For more information, see Amazon ::Aws::ECS
instance role in the
Batch User Guide.
# File 'gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb', line 744
class ComputeResource < Struct.new( :type, :allocation_strategy, :minv_cpus, :maxv_cpus, :desiredv_cpus, :instance_types, :image_id, :subnets, :security_group_ids, :ec2_key_pair, :instance_role, :, :placement_group, :bid_percentage, :spot_iam_fleet_role, :launch_template, :ec2_configuration) SENSITIVE = [] include Aws::Structure end
#instance_types ⇒ Array
<String
> (rw)
The instances types that can be launched. You can specify instance
families to launch any instance type within those families (for
example, c5
or p3
), or you can specify specific sizes within a
family (such as c5.8xlarge
). You can also choose optimal
to
select instance types (from the C4, M4, and R4 instance families)
that match the demand of your job queues.
optimal
uses instance types from the C4, M4, and R4
instance families. In Regions that don't have instance types from
those instance families, instance types from the C5, M5, and R5
instance families are used.
# File 'gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb', line 744
class ComputeResource < Struct.new( :type, :allocation_strategy, :minv_cpus, :maxv_cpus, :desiredv_cpus, :instance_types, :image_id, :subnets, :security_group_ids, :ec2_key_pair, :instance_role, :, :placement_group, :bid_percentage, :spot_iam_fleet_role, :launch_template, :ec2_configuration) SENSITIVE = [] include Aws::Structure end
#launch_template ⇒ Types::LaunchTemplateSpecification (rw)
The launch template to use for your compute resources. Any other compute resource parameters that you specify in a CreateComputeEnvironment API operation override the same parameters in the launch template. You must specify either the launch template ID or launch template name in the request, but not both. For more information, see Launch template support in the Batch User Guide.
# File 'gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb', line 744
class ComputeResource < Struct.new( :type, :allocation_strategy, :minv_cpus, :maxv_cpus, :desiredv_cpus, :instance_types, :image_id, :subnets, :security_group_ids, :ec2_key_pair, :instance_role, :, :placement_group, :bid_percentage, :spot_iam_fleet_role, :launch_template, :ec2_configuration) SENSITIVE = [] include Aws::Structure end
#maxv_cpus ⇒ Integer
(rw)
The maximum number of vCPUs that a compute environment can support.
BEST_FIT_PROGRESSIVE
, SPOT_CAPACITY_OPTIMIZED
and
SPOT_PRICE_CAPACITY_OPTIMIZED
allocation strategies using
On-Demand or Spot Instances, and the BEST_FIT
strategy using Spot
Instances, ::Aws::Batch
might need to exceed maxvCpus
to meet your
capacity requirements. In this event, ::Aws::Batch
never exceeds maxvCpus
by more than a single instance. For example, no more than a single
instance from among those specified in your compute environment is
allocated.
# File 'gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb', line 744
class ComputeResource < Struct.new( :type, :allocation_strategy, :minv_cpus, :maxv_cpus, :desiredv_cpus, :instance_types, :image_id, :subnets, :security_group_ids, :ec2_key_pair, :instance_role, :, :placement_group, :bid_percentage, :spot_iam_fleet_role, :launch_template, :ec2_configuration) SENSITIVE = [] include Aws::Structure end
#minv_cpus ⇒ Integer
(rw)
The minimum number of vCPUs that a compute environment should
maintain (even if the compute environment is DISABLED
).
# File 'gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb', line 744
class ComputeResource < Struct.new( :type, :allocation_strategy, :minv_cpus, :maxv_cpus, :desiredv_cpus, :instance_types, :image_id, :subnets, :security_group_ids, :ec2_key_pair, :instance_role, :, :placement_group, :bid_percentage, :spot_iam_fleet_role, :launch_template, :ec2_configuration) SENSITIVE = [] include Aws::Structure end
#placement_group ⇒ String
(rw)
The Amazon ::Aws::EC2
placement group to associate with your compute
resources. If you intend to submit multi-node parallel jobs to your
compute environment, you should consider creating a cluster
placement group and associate it with your compute resources. This
keeps your multi-node parallel job on a logical grouping of
instances within a single Availability Zone with high network flow
potential. For more information, see Placement groups in the
Amazon ::Aws::EC2
User Guide for Linux Instances.
# File 'gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb', line 744
class ComputeResource < Struct.new( :type, :allocation_strategy, :minv_cpus, :maxv_cpus, :desiredv_cpus, :instance_types, :image_id, :subnets, :security_group_ids, :ec2_key_pair, :instance_role, :, :placement_group, :bid_percentage, :spot_iam_fleet_role, :launch_template, :ec2_configuration) SENSITIVE = [] include Aws::Structure end
#security_group_ids ⇒ Array
<String
> (rw)
The Amazon ::Aws::EC2
security groups that are associated with instances
launched in the compute environment. One or more security groups
must be specified, either in securityGroupIds
or using a launch
template referenced in launchTemplate
. This parameter is required
for jobs that are running on Fargate resources and must contain at
least one security group. Fargate doesn't support launch templates.
If security groups are specified using both securityGroupIds
and
launchTemplate
, the values in securityGroupIds
are used.
# File 'gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb', line 744
class ComputeResource < Struct.new( :type, :allocation_strategy, :minv_cpus, :maxv_cpus, :desiredv_cpus, :instance_types, :image_id, :subnets, :security_group_ids, :ec2_key_pair, :instance_role, :, :placement_group, :bid_percentage, :spot_iam_fleet_role, :launch_template, :ec2_configuration) SENSITIVE = [] include Aws::Structure end
#spot_iam_fleet_role ⇒ String
(rw)
The Amazon ::Aws::Batch::Resource
Name (ARN) of the Amazon EC2 Spot Fleet ::Aws::IAM
role
applied to a SPOT
compute environment. This role is required if
the allocation strategy set to BEST_FIT
or if the allocation
strategy isn't specified. For more information, see Amazon ::Aws::EC2
spot fleet role in the Batch User Guide.
To tag your Spot Instances on creation, the Spot Fleet ::Aws::IAM
role
specified here must use the newer AmazonEC2SpotFleetTaggingRole
managed policy. The previously recommended
AmazonEC2SpotFleetRole managed policy doesn't have the required
permissions to tag Spot Instances. For more information, see Spot
instances not tagged on creation in the Batch User Guide.
# File 'gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb', line 744
class ComputeResource < Struct.new( :type, :allocation_strategy, :minv_cpus, :maxv_cpus, :desiredv_cpus, :instance_types, :image_id, :subnets, :security_group_ids, :ec2_key_pair, :instance_role, :, :placement_group, :bid_percentage, :spot_iam_fleet_role, :launch_template, :ec2_configuration) SENSITIVE = [] include Aws::Structure end
#subnets ⇒ Array
<String
> (rw)
The VPC subnets where the compute resources are launched. These subnets must be within the same VPC. Fargate compute resources can contain up to 16 subnets. For more information, see VPCs and subnets in the Amazon VPC User Guide.
::Aws::Batch
on Amazon EC2 and ::Aws::Batch
on Amazon EKS support Local Zones. For
more information, see Local Zones in the Amazon ::Aws::EC2
User
Guide for Linux Instances, Amazon ::Aws::EKS
and Amazon Web Services
Local Zones in the Amazon ::Aws::EKS
User Guide and Amazon ECS
clusters in Local Zones, Wavelength Zones, and Amazon Web Services
Outposts in the Amazon ::Aws::ECS
Developer Guide.
Batch on Fargate doesn't currently support Local Zones.
# File 'gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb', line 744
class ComputeResource < Struct.new( :type, :allocation_strategy, :minv_cpus, :maxv_cpus, :desiredv_cpus, :instance_types, :image_id, :subnets, :security_group_ids, :ec2_key_pair, :instance_role, :, :placement_group, :bid_percentage, :spot_iam_fleet_role, :launch_template, :ec2_configuration) SENSITIVE = [] include Aws::Structure end
#tags ⇒ Hash
<String
,String
> (rw)
Key-value pair tags to be applied to ::Aws::EC2
resources that are launched
in the compute environment. For Batch, these take the form of
"String1": "String2"
, where String1
is the tag key and String2
is the tag value-for example, \{ "Name": "Batch Instance -
C4OnDemand" \}
. This is helpful for recognizing your ::Aws::Batch
instances in the Amazon EC2 console. Updating these tags requires an
infrastructure update to the compute environment. For more
information, see Updating compute environments in the Batch
User Guide. These tags aren't seen when using the ::Aws::Batch
ListTagsForResource
API operation.
# File 'gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb', line 744
class ComputeResource < Struct.new( :type, :allocation_strategy, :minv_cpus, :maxv_cpus, :desiredv_cpus, :instance_types, :image_id, :subnets, :security_group_ids, :ec2_key_pair, :instance_role, :, :placement_group, :bid_percentage, :spot_iam_fleet_role, :launch_template, :ec2_configuration) SENSITIVE = [] include Aws::Structure end
#type ⇒ String
(rw)
The type of compute environment: EC2
, SPOT
, FARGATE
, or
FARGATE_SPOT
. For more information, see Compute environments
in the Batch User Guide.
If you choose SPOT
, you must also specify an Amazon EC2 Spot Fleet
role with the spotIamFleetRole
parameter. For more information,
see Amazon ::Aws::EC2
spot fleet role in the Batch User Guide.
# File 'gems/aws-sdk-batch/lib/aws-sdk-batch/types.rb', line 744
class ComputeResource < Struct.new( :type, :allocation_strategy, :minv_cpus, :maxv_cpus, :desiredv_cpus, :instance_types, :image_id, :subnets, :security_group_ids, :ec2_key_pair, :instance_role, :, :placement_group, :bid_percentage, :spot_iam_fleet_role, :launch_template, :ec2_configuration) SENSITIVE = [] include Aws::Structure end


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#include <OpticalObject.h>
Public Member Functions | |
| OpticalObject () | |
| OpticalObject (const OpticalObject &) | |
| OpticalObject (CvConnectedComp *) | |
| virtual | ~OpticalObject () |
| double | area () const |
| Gets object area in pixels. | |
| CvRect | rect () const |
| Gets bounding rect. | |
| CvConnectedComp * | connectedComponents () const |
| Mask * | toMask () const |
| Draws object in Mask. | |
| OpticalObject * | convexHull (int=Clockwise, int=0) |
| int | checkConvexity () |
| bool | isOnRect (const CvRect &) |
| Returns true if object is over rect. | |
| double | distanceTo (const CvPoint &pt) |
| Calculates minimum distance to point. | |
| double | distanceTo (const CvRect &r) |
| Calculates minimum distance to center of rect. | |
Static Public Member Functions | |
| static OpticalObjects * | opticalObjectsFromImage (const Image *, double=32.0l, int=ChainApproxSimple) |
| Default Constructor Find's all optical objects on image and returns it in a vector. | |
| static Mask * | maskFromUnconnectedMask (const Mask *, double=32.0l) |
| Generates boolean mask with connected components. | |
| static OpticalObjects * | convexityDefects (OpticalObject *, OpticalObject *) |
Protected Attributes | |
| CvConnectedComp * | m_objectComponents |
Static Private Member Functions | |
| static CvSeq * | polygonalContours (Image *, int, double) |
| OpenCV::VectorizedGraphics::OpticalObject::OpticalObject | ( | ) |
Referenced by convexHull(), and opticalObjectsFromImage().
| OpenCV::VectorizedGraphics::OpticalObject::OpticalObject | ( | const OpticalObject & | optObj | ) |
| OpenCV::VectorizedGraphics::OpticalObject::OpticalObject | ( | CvConnectedComp * | comp | ) | [explicit] |
| OpenCV::VectorizedGraphics::OpticalObject::~OpticalObject | ( | ) | [virtual] |
References m_objectComponents.
| double OpenCV::VectorizedGraphics::OpticalObject::area | ( | ) | const |
| int OpenCV::VectorizedGraphics::OpticalObject::checkConvexity | ( | ) | [inline] |
| CvConnectedComp* OpenCV::VectorizedGraphics::OpticalObject::connectedComponents | ( | ) | const [inline] |
| OpticalObject * OpenCV::VectorizedGraphics::OpticalObject::convexHull | ( | int | orientation = Clockwise , |
|
| int | pointsCount = 0 | |||
| ) |
| OpticalObjects * OpenCV::VectorizedGraphics::OpticalObject::convexityDefects | ( | OpticalObject * | , | |
| OpticalObject * | ||||
| ) | [static] |
| double OpenCV::VectorizedGraphics::OpticalObject::distanceTo | ( | const CvRect & | r | ) | [inline] |
| double OpenCV::VectorizedGraphics::OpticalObject::distanceTo | ( | const CvPoint & | pt | ) | [inline] |
Calculates minimum distance to point.
References rect().
Referenced by OpenCV::Tracking::ObjectTracker::ObjectTracker().
| bool OpenCV::VectorizedGraphics::OpticalObject::isOnRect | ( | const CvRect & | superRect | ) |
Returns true if object is over rect.
References CV_RECT_AREA(), and rect().
Referenced by OpenCV::Tracking::ObjectTracker::ObjectTracker().
| Mask * OpenCV::VectorizedGraphics::OpticalObject::maskFromUnconnectedMask | ( | const Mask * | mask, | |
| double | minPerimeter = 32.0l | |||
| ) | [static] |
Generates boolean mask with connected components.
| [in] | mask | Mask to be processed. |
| [in] | minPerimeter | Minimum Object's perimeter. |
References OpenCV::VectorizedGraphics::ChainApproxSimple, OpenCV::Bitmap::Image::clone(), OpenCV::Bitmap::Image::convert(), OpenCV::Bitmap::Image::cvImage(), OpenCV::Bitmap::Image::numberOfChannels(), polygonalContours(), OpenCV::Bitmap::Image::size(), and OpenCV::Bitmap::UByte.
| OpticalObjects * OpenCV::VectorizedGraphics::OpticalObject::opticalObjectsFromImage | ( | const Image * | image, | |
| double | minPerimeter = 32.0l , |
|||
| int | method = ChainApproxSimple | |||
| ) | [static] |
Default Constructor Find's all optical objects on image and returns it in a vector.
| [in] | image | Image to be processed. |
| [in] | minPerimeter | Minimum Object's perimeter. |
| [in] | method | Algorithm to find objects. |
References OpenCV::Bitmap::Image::clone(), OpenCV::Bitmap::Image::convert(), OpenCV::VectorizedGraphics::CV_RECT_ZERO, OpenCV::Bitmap::Image::numberOfChannels(), OpticalObject(), and polygonalContours().
Referenced by OpenCV::Tracking::ObjectTracker::ObjectTracker(), and OpenCV::Tracking::ObjectTracker::trackObject().
| CvSeq * OpenCV::VectorizedGraphics::OpticalObject::polygonalContours | ( | Image * | image, | |
| int | method, | |||
| double | minPerimeter | |||
| ) | [static, private] |
| CvRect OpenCV::VectorizedGraphics::OpticalObject::rect | ( | ) | const |
Gets bounding rect.
References CV_RECT_EQUAL(), OpenCV::VectorizedGraphics::CV_RECT_ZERO, and m_objectComponents.
Referenced by distanceTo(), isOnRect(), and OpenCV::Tracking::ObjectTracker::trackObject().
| Mask * OpenCV::VectorizedGraphics::OpticalObject::toMask | ( | ) | const |
Draws object in Mask.
References OpenCV::Bitmap::DrawableImage::drawObject(), and OpenCV::Bitmap::UByte.
CvConnectedComp* OpenCV::VectorizedGraphics::OpticalObject::m_objectComponents [protected] |
Referenced by area(), checkConvexity(), connectedComponents(), convexHull(), rect(), and ~OpticalObject().


xlsfonts(1) xlsfonts(1) NAME xlsfonts - server font list displayer for X SYNOPSIS xlsfonts [-display host:dpy] [-l] [-ll] [-lll] [-m] [-C] [-1] [-w width] [-n columns] [-u] [-o] [-fn pattern] DESCRIPTION xlsfonts lists the fonts that match the given pattern. The wildcard character "*" may be used to match any sequence of characters (including none), and "?" to match any single character. If no pattern is given, "*" is assumed. The "*" and "?" characters must be quoted to prevent them from being expanded by the shell. OPTIONS -display host:dpy This option specifies the X server to contact. -d host:dpy Same as -display host:dpy. -l Lists some attributes of the font on one line in addition to its name. -ll Lists font properties in addition to -l output. -lll Lists character metrics in addition to -ll output. -m This option indicates that long listings should also print the minimum and maximum bounds of each font. -C This option indicates that listings should use multiple columns. This is the same as -n 0. -1 This option indicates that listings should use a single column. This is the same as -n 1. -w width This option specifies the width in characters that should be used in figuring out how many columns to print. The default is 79. -n columns This option specifies the number of columns to use in displaying the output. By default, it will attempt to fit as many columns of font names into the number of character specified by -w width. -u This option indicates that the output should be left unsorted. -o This option indicates that xlsfonts should do an OpenFont (and QueryFont, if appropriate) rather than a ListFonts. This is use‐ ful if ListFonts or ListFontsWithInfo fail to list a known font (as is the case with some scaled font systems). -fn pattern This option specifies the font name pattern to match. SEE ALSO X(7), Xserver(1), xset(1), xfd(1), X Logical Font Description Conventions ENVIRONMENT DISPLAY DISPLAY must be set to get the default host and display to use. BUGS Doing ``xlsfonts -l'' can tie up your server for a very long time. This is really a bug with single-threaded non-preemptable servers, not with this program. AUTHOR Mark Lillibridge Jim Fulton Phil Karlton Roland Mainz X Version 11 xlsfonts 1.0.4 xlsfonts(1)


Maximum Likelihood Estimation (Generic models)¶
This tutorial explains how to quickly implement new maximum likelihood models in statsmodels
. We give two examples:
- Probit model for binary dependent variables
- Negative binomial model for count data
The GenericLikelihoodModel
class eases the process by providing tools such as automatic numeric differentiation and a unified interface to scipy
optimization functions. Using statsmodels
, users can fit new MLE models simply by "plugging-in" a log-likelihood function.
Example 1: Probit model¶
from __future__ import print_function
import numpy as np
from scipy import stats
import statsmodels.api as sm
from statsmodels.base.model import GenericLikelihoodModel
The Spector
dataset is distributed with statsmodels
. You can access a vector of values for the dependent variable (endog
) and a matrix of regressors (exog
) like this:
data = sm.datasets.spector.load_pandas()
exog = data.exog
endog = data.endog
print(sm.datasets.spector.NOTE)
print(data.exog.head())
Them, we add a constant to the matrix of regressors:
exog = sm.add_constant(exog, prepend=True)
To create your own Likelihood Model, you simply need to overwrite the loglike method.
class MyProbit(GenericLikelihoodModel):
def loglike(self, params):
exog = self.exog
endog = self.endog
q = 2 * endog - 1
return stats.norm.logcdf(q*np.dot(exog, params)).sum()
Estimate the model and print a summary:
sm_probit_manual = MyProbit(endog, exog).fit()
print(sm_probit_manual.summary())
Compare your Probit implementation to statsmodels
' "canned" implementation:
sm_probit_canned = sm.Probit(endog, exog).fit()
print(sm_probit_canned.params)
print(sm_probit_manual.params)
print(sm_probit_canned.cov_params())
print(sm_probit_manual.cov_params())
Notice that the GenericMaximumLikelihood
class provides automatic differentiation, so we didn't have to provide Hessian or Score functions in order to calculate the covariance estimates.
Example 2: Negative Binomial Regression for Count Data¶
Consider a negative binomial regression model for count data with log-likelihood (type NB-2) function expressed as:
$$ \mathcal{L}(\beta_j; y, \alpha) = \sum_{i=1}^n y_i ln \left ( \frac{\alpha exp(X_i'\beta)}{1+\alpha exp(X_i'\beta)} \right ) - \frac{1}{\alpha} ln(1+\alpha exp(X_i'\beta)) + ln \Gamma (y_i + 1/\alpha) - ln \Gamma (y_i+1) - ln \Gamma (1/\alpha) $$with a matrix of regressors $X$, a vector of coefficients $\beta$, and the negative binomial heterogeneity parameter $\alpha$.
Using the nbinom
distribution from scipy
, we can write this likelihood
simply as:
import numpy as np
from scipy.stats import nbinom
def _ll_nb2(y, X, beta, alph):
mu = np.exp(np.dot(X, beta))
size = 1/alph
prob = size/(size+mu)
ll = nbinom.logpmf(y, size, prob)
return ll
New Model Class¶
We create a new model class which inherits from GenericLikelihoodModel
:
from statsmodels.base.model import GenericLikelihoodModel
class NBin(GenericLikelihoodModel):
def __init__(self, endog, exog, **kwds):
super(NBin, self).__init__(endog, exog, **kwds)
def nloglikeobs(self, params):
alph = params[-1]
beta = params[:-1]
ll = _ll_nb2(self.endog, self.exog, beta, alph)
return -ll
def fit(self, start_params=None, maxiter=10000, maxfun=5000, **kwds):
# we have one additional parameter and we need to add it for summary
self.exog_names.append('alpha')
if start_params == None:
# Reasonable starting values
start_params = np.append(np.zeros(self.exog.shape[1]), .5)
# intercept
start_params[-2] = np.log(self.endog.mean())
return super(NBin, self).fit(start_params=start_params,
maxiter=maxiter, maxfun=maxfun,
**kwds)
Two important things to notice:
nloglikeobs
: This function should return one evaluation of the negative log-likelihood function per observation in your dataset (i.e. rows of the endog/X matrix).start_params
: A one-dimensional array of starting values needs to be provided. The size of this array determines the number of parameters that will be used in optimization.
That's it! You're done!
Usage Example¶
The Medpar
dataset is hosted in CSV format at the Rdatasets repository. We use the read_csv
function from the Pandas library to load the data
in memory. We then print the first few columns:
import statsmodels.api as sm
medpar = sm.datasets.get_rdataset("medpar", "COUNT", cache=True).data
medpar.head()
The model we are interested in has a vector of non-negative integers as
dependent variable (los
), and 5 regressors: Intercept
, type2
,
type3
, hmo
, white
.
For estimation, we need to create two variables to hold our regressors and the outcome variable. These can be ndarrays or pandas objects.
y = medpar.los
X = medpar[["type2", "type3", "hmo", "white"]]
X["constant"] = 1
Then, we fit the model and extract some information:
mod = NBin(y, X)
res = mod.fit()
Extract parameter estimates, standard errors, p-values, AIC, etc.:
print('Parameters: ', res.params)
print('Standard errors: ', res.bse)
print('P-values: ', res.pvalues)
print('AIC: ', res.aic)
As usual, you can obtain a full list of available information by typing
dir(res)
.
We can also look at the summary of the estimation results.
print(res.summary())
Testing¶
We can check the results by using the statsmodels implementation of the Negative Binomial model, which uses the analytic score function and Hessian.
res_nbin = sm.NegativeBinomial(y, X).fit(disp=0)
print(res_nbin.summary())
print(res_nbin.params)
print(res_nbin.bse)
Or we could compare them to results obtained using the MASS implementation for R:
url = 'http://vincentarelbundock.github.com/Rdatasets/csv/COUNT/medpar.csv'
medpar = read.csv(url)
f = los~factor(type)+hmo+white
library(MASS)
mod = glm.nb(f, medpar)
coef(summary(mod))
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.31027893 0.06744676 34.253370 3.885556e-257
factor(type)2 0.22124898 0.05045746 4.384861 1.160597e-05
factor(type)3 0.70615882 0.07599849 9.291748 1.517751e-20
hmo -0.06795522 0.05321375 -1.277024 2.015939e-01
white -0.12906544 0.06836272 -1.887951 5.903257e-02
Numerical precision¶
The statsmodels
generic MLE and R
parameter estimates agree up to the fourth decimal. The standard errors, however, agree only up to the second decimal. This discrepancy is the result of imprecision in our Hessian numerical estimates. In the current context, the difference between MASS
and statsmodels
standard error estimates is substantively irrelevant, but it highlights the fact that users who need very precise estimates may not always want to rely on default settings when using numerical derivatives. In such cases, it is better to use analytical derivatives with the LikelihoodModel
class.


Types of abiotics stress
Real time updates
Orchard microclimate
Fruit surface temperature
Canopy water stress


A wrapper for the C wrapper for the original fortran version of the L-BFGS-B algorithm. The main class is called {@link lbfgsb.Minimizer}.


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- Disney+ begins streaming for users on November 12.
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Originally expected to arrive on the service December 11 was Avengers: Endgame, which Disney announced last week that it would now be available at launch. Today on Twitter, Disney confirmed that Iron Man 2, Thor, Captain America: The First Avenger, Captain America: The Winter Soldier, The Avengers, Guardians of the Galaxy, Captain America: Civil War, Doctor Strange, and Guardians of the Galaxy Vol. 2 would also be ready to go at launch. The only films that will be missing initially are Black Panther, Ant-Man and the Wasp, Avengers: Infinity War, and Thor: Ragnarok, which will be showing up at Disney+ after the contract with Netflix has expired — which should be early 2020.
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For the top 5% of stocks by Average Dollar Volume, compute the regression line y=ax+b If the slope exceeds 10% per year, and the past drawdown crosses the regression line, then long/short the stock. Long if the trend is up, short if the trend is down. While holding on to a long/short position, set a stop loss if the current drawdown exceeds the drawdown at time of entry.


slipbox
is a static site generator for Zettelkasten notes.
GitHub: https://github.com/lggruspe/slipbox
Slipbox: https://lggruspe.github.io/slipbox
Source: https://github.com/lggruspe/slipbox/blob/master/docs/index.md
Make sure you have pandoc
, pandoc-citeproc
and python
installed.
slipbox init
The initialized directory contains a .slipbox
directory. Inside it you’ll find a configuration file, an sqlite3 database and a patterns file.
slipbox build
slipbox build
searches the slipbox directory for notes recursively. It considers every file in the directory that matches the glob patterns in .slipbox/patterns
.
You can store each note in a separate file, or you can put multiple notes together in one file (a slipbox note is just a section in a document).
Slipbox notes begin with a level 1 header. The header text must contain an ID and a title.
You can navigate to a specific note in the browser by replacing the URL hash with the ID of the note.
Links must either be absolute or relative to the output HTML. Suppose your slipbox directory has the following structure.
Example:
.slipbox/config.cfg
The config file gets created when you run slipbox init
. Here’s a sample config file that uses mathjax
and citeproc
.
// .slipbox/config.cfg
[slipbox]
content_options = --mathjax --bibliography my-bib.bib
document_options = --mathjax --o output.html -c my-css.css
convert_to_data_url = False
content_options
and document_options
are both passed to Pandoc.
content_options
document_options
slipbox
provides multiple ways of connecting notes.
slipbox uses a Pandoc filter to convert hashtags into tags. You can click on tags to get a list of other notes that have the same tag.
The #tags
page contains a list of all tags in use.
To link to a note in the slipbox, use the note’s ID as the link target. slipbox automatically creates backlinks from direct links.
The links can be seen in the interactive graph below. [#11]
Note pages contain a graph of notes related to the current note at the bottom of the page.
It shows
A tagged link is a hashtag followed by a slash and a note ID. Example: #example/11.
This connects the current note to the linked note.
The purpose of the tag is to distinguish between different levels of sequences.
These links show up in the interactive graph as dashed lines.
There’s a search bar at the top of the page that you can use to look for notes.
slipbox
uses pandoc-citeproc
for citations. Clicking on a citation will open a page with a list of all notes that cite the same reference.
Here’s how a citation looks: (Author 2020).
To activate this feature, you need to specify a bibliography file in the config file.
The #references
page contains a list of all cited references.
Author. 2020. Title.


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Get notified when we launch the EDU token.
We respect your privacy. E-mail addresses will only be used to notify you once we reach one of our five milestones.


4.2 O-Notation
O -notation is the most
common notation used to express an algorithm's performance in
a formal manner. Formally, O
-notation expresses the upper bound of a function within a
constant factor. Specifically, if g (n)
is an upper bound of f (n), then for some constant c it is possible to find a value of
n, call it n0, for which any value of
n n0 will
result in f (n) cg (n).
Normally we express an algorithm's
performance as a function of the size of the data it
processes. That is, for some data of size n, we describe its performance with
some function f (n). However, while in many cases we
can determine f exactly,
usually it is not necessary to be this precise. Primarily we
are interested only in the growth
rate of f, which
describes how quickly the algorithm's performance will degrade
as the size of the data it processes becomes arbitrarily
large. An algorithm's growth rate, or order of growth, is significant
because ultimately it describes how efficient the algorithm is for arbitrary inputs.
O -notation reflects an
algorithm's order of growth.
4.2.1 Simple Rules for O-Notation
When we look at some function f (n)
in terms of its growth rate, a few things become apparent.
First, we can ignore constant terms because as the value of
n becomes larger and larger,
eventually constant terms will become insignificant. For
example, if T (n) = n
+ 50 describes the running time of an algorithm, and
n, the size of the data it
processes, is only 1024, the constant term in this expression
already constitutes less than 5% of the running time. Second,
we can ignore constant multipliers of terms because they too
will become insignificant as the value of n increases. For example, if T1(n) =
n2 and T2(n) = 10n describe the running times of two
algorithms for solving the same problem, n only has to be greater than 10 for
T1 to become greater
than T2. Finally, we
need only consider the highest-order term because, again, as
n increases, higher-order terms
quickly outweigh the lower-order ones. For example, if T (n)
= n2 + n describes the running time of an
algorithm, and n is 1024, the
lesser-order term of this expression constitutes less than
0.1% of the running time. These ideas are formalized in the
following simple rules for expressing functions in O -notation.
-
Constant terms are expressed as O (1). When analyzing the running
time of an algorithm, apply this rule when you have a task
that you know will execute in a certain amount of time
regardless of the size of the data it processes. Formally
stated, for some constant c:
O(c) = O(1)
-
Multiplicative constants are omitted. When
analyzing the running time of an algorithm, apply this rule
when you have a number of tasks that all execute in the same
amount of time. For example, if three tasks each run in time
T (n) = n, the result is O (3n), which simplifies to O (n). Formally stated, for some
constant c:
O(cT) = cO(T) =
O(T)
-
Addition is performed by taking the
maximum. When analyzing the running time of an algorithm,
apply this rule when one task is executed after another. For
example, if T1(n) = n and T2(n) = n2 describe two tasks
executed sequentially, the result is O (n) + O (n2), which simplifies to
O (n2). Formally
stated:
O(T1)+O(T1+T2) = max (O(T1), O(T2))
-
Multiplication is not changed but often is
rewritten more compactly. When analyzing the running time of
an algorithm, apply this rule when one task causes another
to be executed some number of times for each iteration of
itself. For example, in a nested loop whose outer iterations
are described by T1 and whose inner
iterations by T2,
if T1(n) = n and T2(n) = n, the result is O (n)O
(n), or O (n2). Formally
stated:
O(T1)O(T2) = O(T1 T2)
4.2.2 O-Notation Example and Why It
Works
The next section discusses how these
rules help us in predicting an algorithm's performance. For
now, let's look at a specific example demonstrating why they
work so well in describing a function's growth rate. Suppose
we have an algorithm whose running time is described by the
function T (n) = 3n2 + 10n + 10. Using the rules of O -notation, this function can be
simplified to:
O(T(n)) =
O(3n2 + 10n + 10) = O(3n2) = O(n2)
This indicates that the term containing n2 will be the one that
accounts for most of the running time as n grows arbitrarily large. We can
verify this quantitatively by computing the percentage of the
overall running time that each term accounts for as n increases. For example, when n = 10, we have the following:
- Running time for 3n2:
3(10)2/(3(10)2 + 10(10) + 10) = 73.2%
- Running time for 10n:
10(10)/(3(10)2 + 10(10) + 10) = 24.4%
- Running time for 10: 10/(3(10)2 + 10(10) +
10) = 2.4%
Already we see that the n2 term accounts for the
majority of the overall running time. Now consider when n = 100:
- Running time for 3n2:
3(100)2/(3(100)2 + 10(100) + 10) =
96.7% (Higher)
- Running time for 10n:
10(100)/(3(100)2 + 10(100) + 10) = 3.2% (Lower)
- Running time for 10: 10/(3(100)2 + 10(100) +
10) < 0.1% (Lower)
Here we see that this term accounts for almost all of the running time, while
the significance of the other terms diminishes further.
Imagine how much of the running time this term would account
for if n were
106!
|


Rooke Rivas
27
21
1
1
1
1
1
Nimble
Daft
Rooke is not really perfect at anything, but Rooke tries to do their best! Rooke has remarkable kinesthetic intelligence. Rooke seem to dance around danger with preternatural grace.
Rooke is not really perfect at anything, but Rooke tries to do their best! Rooke has remarkable kinesthetic intelligence. Rooke seem to dance around danger with preternatural grace.


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Zend Framework
LICENSE
This source file is subject to the new BSD license that is bundled with this package in the file LICENSE.txt. It is also available through the world-wide-web at this URL: http://framework.zend.com/license/new-bsd If you did not receive a copy of the license and are unable to obtain it through the world-wide-web, please send an email to<EMAIL_ADDRESS>so we can send you a copy immediately.
array
$_caching= 'array'
Indexed map of Actions to attempt Page caching on by Controller
array
Detailsarray
$_extensions= 'array'
Indexed map of Extensions by Controller and Action
array
Details__call(
string $method, array $args
)
:
mixed
Proxy non-matched methods back to Zend_Cache_Manager where appropriate
| Name | Type | Description |
|---|---|---|
| $method | string | |
| $args | array |
| Type | Description |
|---|---|
| mixed |
_encodeCacheId(
string $requestUri
)
:
string
Encode a Cache ID as hexadecimal. This is a workaround because Backend ID validation is trapped in the Frontend classes. Will try to get this reversed for ZF 2.0 because it's a major annoyance to have IDs so restricted!
| Name | Type | Description |
|---|---|---|
| $requestUri | string |
| Type | Description |
|---|---|
| string |
direct(
array $actions, array $tags
=
array, $extension
=
null
)
:
void
Tell the helper which actions are cacheable and under which tags (if applicable) they should be recorded with
| Name | Type | Description |
|---|---|---|
| $actions | array | |
| $tags | array | |
| $extension |
getActionController(
)
:
\Zend_Controller_Action
Retrieve current action controller
Inherited from: \Zend_Controller_Action_Helper_Abstract::getActionController()| Type | Description |
|---|---|
| \Zend_Controller_Action |
getCacheableActions(
)
:
array
Return a list of actions for the current Controller marked for caching
| Type | Description |
|---|---|
| array |
getCacheableTags(
)
:
array
Return a list of tags set for all cacheable actions
| Type | Description |
|---|---|
| array |
getFrontController(
)
:
\Zend_Controller_Front
Retrieve front controller instance
Inherited from: \Zend_Controller_Action_Helper_Abstract::getFrontController()| Type | Description |
|---|---|
| \Zend_Controller_Front |
getManager(
)
:
\Zend_Cache_Manager
Get the Cache Manager instance or instantiate the object if not exists. Attempts to load from bootstrap if available.
| Type | Description |
|---|---|
| \Zend_Cache_Manager |
getName(
)
:
string
getName()
Inherited from: \Zend_Controller_Action_Helper_Abstract::getName()| Type | Description |
|---|---|
| string |
getRequest(
)
:
\Zend_Controller_Request_Abstract
getRequest() -
Inherited from: \Zend_Controller_Action_Helper_Abstract::getRequest()| Type | Description |
|---|---|
| \Zend_Controller_Request_Abstract | $request |
getResponse(
)
:
\Zend_Controller_Response_Abstract
getResponse() -
Inherited from: \Zend_Controller_Action_Helper_Abstract::getResponse()| Type | Description |
|---|---|
| \Zend_Controller_Response_Abstract | $response |
init(
)
:
void
Hook into action controller initialization
Inherited from: \Zend_Controller_Action_Helper_Abstract::init()postDispatch(
)
:
void
Hook into action controller postDispatch() workflow
Inherited from: \Zend_Controller_Action_Helper_Abstract::postDispatch()removePage(
string $relativeUrl, bool $recursive
=
false
)
:
mixed
Remove a specific page cache static file based on its relative URL from the application's public directory.
The file extension is not required here; usually matches the original REQUEST_URI that was cached.
| Name | Type | Description |
|---|---|---|
| $relativeUrl | string | |
| $recursive | bool |
| Type | Description |
|---|---|
| mixed |
removePagesTagged(
array $tags
)
:
mixed
Remove a specific page cache static file based on its relative URL from the application's public directory.
The file extension is not required here; usually matches the original REQUEST_URI that was cached.
| Name | Type | Description |
|---|---|---|
| $tags | array |
| Type | Description |
|---|---|
| mixed |
setActionController(
\Zend_Controller_Action $actionController
=
null
)
:
\Zend_Controller_ActionHelper_Abstract
setActionController()
Inherited from: \Zend_Controller_Action_Helper_Abstract::setActionController()| Name | Type | Description |
|---|---|---|
| $actionController | \Zend_Controller_Action |
| Type | Description |
|---|---|
| \Zend_Controller_ActionHelper_Abstract | Provides a fluent interface |
setManager(
\Zend_Cache_Manager $manager
)
:
void
Set an instance of the Cache Manager for this helper
| Name | Type | Description |
|---|---|---|
| $manager | \Zend_Cache_Manager |


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book for you. Practical Ajax Projects with Java Technology provides
the ultimate learn-by-example experience, featuring seven complete
example applications for you to learn from and then adapt for use in
your own projects. During each application, the author will lead you
through the planning, design, and implementation stages.
32.99 per unit |
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Atletico defender Savic suffers injury setback
by BeSoccerAtletico Madrid are no closer to having Stefan Savic back in action after he suffered a relapse of his hamstring problem.
Stefan Savic has suffered a setback as he bids to return from a hamstring injury.
The Atletico Madrid centre-back has been out of action since Montenegro's 0-0 draw against Bulgaria in Euro 2020 qualifying on October 11.
Featured links
- BREAKING NEWS: Mourinho replaces Pochettino at Tottenham
- The latest football transfers news and rumours
- Wales, golf, Madrid... in that order
- Luis Enrique returns to Spain national team
Atleti announced Savic, who was substituted after 39 minutes of that match, had suffered a hamstring issue but did not offer an estimated recovery time.
And the club announced on Wednesday the defender "suffered a relapse of the grade II injury".
Savic has undergone tests and, according to Atleti's short statement, "is pending response to treatment".


Description
The Tool Bar Sample 1 demonstrates the toolbar, toolbar buttons, and flyover text within a simple editor. The tool bar buttons appear as bitmaps, bitmaps and text, or just text. The toolbar itself can be positioned on the top, bottom, left, right, free floating window, or hidden.
Concept
Sample program demonstrates IToolBar, IToolBarButton, IFlyText, IFlyOverHelpHandler, and IMultiLineEdit.
Task
The toolbar manipulates the multiline entryfield. The toolbars have bitmap, text, and bitmap with text views.
Special Notes:
The sample must run from the sample directory to load a file into the multiline entryfield.
Supported
Supported
Supported


We Heard You
Left click
to move to the next level of content.
Right click
to return to the previous content.
Orientation:
horizontal
vertical
Go to Parent


"Our patrons have high expectations of their library district, and we aim to meet those expectations by providing exceptional library experiences through our signature programs and events, beautiful facilities, contemporary technology, and expansive collections."
arrow_back
Bob Pasicznyuk
Executive Director
Bob Pasicznyuk reunited with Douglas County Libraries in 2014 after a five-year hiatus leading the Cedar Rapids Public Library in a time of rapid change and rebuilding. Previously, Bob managed projects and technology efforts in libraries at Douglas County, Pikes Peak Library District, and the University of Colorado. He has an M.S. in Library Science and Certificate of Advanced Study in Information Science from the University of North Texas.
Art Gloverarrow_forward
Human Resources Director
"The employees at Douglas County Libraries are a tremendous asset — they’re the face of our organization. By embracing the core values of this organization, our employees seek to create positive experiences with every personal interaction."
arrow_back
Art Glover
Human Resources Director
Art Glover has been with Douglas County Libraries since 2003. He is certified as a "Senior Professional in Human Resources (SPHR)," an internationally recognized certification in the Human Resources profession. At Douglas County Libraries, Mr. Glover oversees management of the Human Resources, Volunteer Services, and Learning & Development Departments. He holds an M.A. in Human Resources from Webster University (Kansas City, MO).
Karen Garganarrow_forward
Finance Director
"As a tax-funded organization, Douglas County Libraries is accountable to the Douglas County community. We take this role of stewardship seriously. It is our duty and privilege to use those tax dollars wisely and in the most effective ways to the benefit of all patrons."
arrow_back
Karen Gargan
Finance Director
Karen Gargan joined Douglas County Libraries in 2011 with 18 years of leadership in progressive corporate and operational finance, primarily in the manufacturing and food service distribution industries. At Douglas County Libraries, Ms. Gargan oversees the finance and facilities departments. She received her undergraduate degree in Finance from Colorado State University.
Amber DeBerryarrow_forward
Community Relations Director
"Our libraries are more than just learning hubs, they’re the centers of our community. Douglas County Libraries brings the community together, inside and outside our beautiful buildings, with excellent public resources and educational, engaging and entertaining events and programming for all patrons."
arrow_back
Amber DeBerry
Community Relations Director
Amber DeBerry joined Douglas County Libraries in May of 2011. Previously, she was Senior Vice President of Revenue Operations for The Wildlife Experience and in the marketing department of the Denver Zoological Foundation. She has a B.S. in Journalism with a minor in Business from the University of Colorado at Boulder. Over the years, she has been a member of various marketing and foundation committees. At Douglas County Libraries, Amber oversees Community Relations, District Programming, and the Douglas County Libraries Foundation.
Sheila Kerberarrow_forward
Branch Operations Director
"Our libraries, including new, larger branches in Castle Pines, Lone Tree and Parker, allow us to serve patrons in fresh and fun ways. Staff, amenities and materials in all our locations facilitate meeting, studying and learning. Douglas County Libraries truly is building community. I often think I have the best job in the whole world!"
arrow_back
Sheila Kerber
Branch Operations Director
Sheila Kerber has been with Douglas County Libraries since 2007. Previously, she worked for the Library of Congress, the University of Dayton, and the Arapahoe Library District before coming to Douglas County Libraries. Kerber provides the professional leadership to coordinate and supervise all aspects of branch operations, in addition to providing supervisory support to the District's Branch Manager Team and our Business Librarian. She has a B.A. in English Literature and a Master of Library and Information Science.
Sharon Nemechekarrow_forward
Archives & Collections Director
"Collections are the heart of any thriving library. At Douglas County Libraries, we pride ourselves on curating materials that are not only in demand, but that also encourage and strengthen reading and lifelong learning for all patrons, from babies to seniors. It’s extremely gratifying to play such a vital role in helping our community reach its potential."
arrow_back
Sharon Nemechek
Archives & Collections Director
Sharon Nemechek has been with Douglas County Libraries since 2001. She started at the James H. LaRue branch as a substitute librarian, managed the Lone Tree and Parker libraries, and then moved into Support Services as the Collection Services Manager. She has a Master's degree in Library and Information Science from UCLA. Ms. Nemechek oversees the collection development process and the Douglas County History Research Center. Library collections, old and new, are her passion.
Patti Owen-DeLayarrow_forward
Executive Assistant
"Douglas County Libraries’ administrative team takes a thoughtful, forward-looking approach to providing premium library experiences for our guests. All of our strategic plans and goals are designed with the patron top of mind."
arrow_back
Patti Owen-DeLay
Executive Assistant
Patti Owen-DeLay has rejoined Douglas County Libraries after an absence of seven years. During her absence she became a Professional Certified Coach and led coach training programs in the United States and Canada. Recently, she returned to libraries, working for Arapahoe Library District. Back at Douglas County Libraries, she is again Executive Assistant.
Jeff Konishiarrow_forward
Information Technology Director
"Technology is best when it helps, not hinders. At Douglas County Libraries, we strive to give visitors a seamless technology experience — whether they’re using the audiovisual equipment in meeting rooms, public Wi-Fi, print/copy services, public computers, laptop vending, or simply the self-checkouts. We want to exceed patron expectations."
arrow_back
Jeff Konishi
Information Technology Director
Jeff Konishi joined Douglas County Libraries after acting as IT Director for several public sector organizations, including the City of Amarillo and the City of Englewood. His previous experience also includes project management, programming and business analysis, plus various accounting, auditing and budgeting positions. Jeff has a B.S. in Accounting from the University of Northern Colorado and a Masters in Computer Information Systems from the University of Denver. At Douglas County Libraries, Jeff leads the IT team and associated technology endeavors.


You'll love a few cakes with coffee, as an appetizer, as a snack in front of the netflix, or as a low quality treat. We are developing new recipes and always searching for new, bold flavors. You can browse our collection and enjoy in a different universe.


VoizFonica is a Mobile Network Operator and telephone company. VoizFonica provides network access and telecommunication services that help people and companies communicate in an easy, efficient and environmentally friendly way.VoizFonica business is very technology dependent as company acts as a network service provider (NSP) in mobile.VoizFonica is a company that is aware of the new challenges posed by today’s society. This is why the company offers the means to facilitate communication between people, providing them with the most secure and state of the art technology in order for them to live better, and for them to achieve whatever they resolve.


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"Burn the forests if you will, but you will only scorch your own lungs."
-- Anthony T. Hincks


Inflation expectations remain in the gutter
by Unconventional EconomistFrom Roy Morgan Research:
Surveyed in October, Australians as a whole expect prices to increase by 4.1% annually over the next two years. This is up very slightly (0.1%) on how they were feeling in September, but is down to a greater degree (0.4%) on their expectations a year earlier.
There are 888 words left in this subscriber-only article.


Quantum Chess is a varient of conventional chess game, with the power of quantum mechanics.
Rules
There are two type of moves,
When using quantum moves, the result isn't instantaniously presented. According to quantum mechanics, the piece is now in the quantum superposition, which means it's at both postions. However, when two different pieces are in the same square, a measurement at that position has to be done in order to find the winner.
More interestingly, two pieces can be entangled, so the result from a measurement might impact some irrevelent pieces.
To avoid confusion, we eliminate some original rules


{% load staticfiles%}
{% block styles%} {% endblock%}
Independent creative pair. Our goal is to make easy and effective solutions based on our experience and
relevance to your business case.
Sites of the day
Previous Winners
Nowy Teatr
From Poland
October 19, 2019
BY HUNCWOT
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From Czech Republic
October 20, 2019
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From Brazil
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BY JULIO CESAR
Readymag
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BY READYMAG
Editorial New
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October 21, 2019
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Our Place
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BY THE COUCH
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From United States
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Editorial New
From Canada
October 21, 2019
BY LOCOMOTIVE
Our Place
From United States
October 18, 2019
BY THE COUCH
Readymag
From United States
October 18, 2019
BY READYMAG
Editorial New
From Canada
October 21, 2019
BY LOCOMOTIVE
Our Place
From United States
October 18, 2019
BY THE COUCH


Wi-Fix: tap and connect!
Select your Wifi network
Next
Enter your Wifi password
Select authentication type
Select encryption type
Move to an NFC tag


[gecode-users] Space access during ViewSelection?
Christian Schulte
schulte at imit.kth.se
Tue Jul 11 19:55:07 CEST 2006
Yes it will be also for views. The changes should appear in the svn trunk
tomorrow.
Christian
-----Original Message-----
From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
Of Martin Mann
Sent: Tuesday, July 11, 2006 6:07 PM
To: 'gecode user list'
Subject: Re: [gecode-users] Space access during ViewSelection?
for me a const access is enough! (anyway better than static variables)
also const makes sense for me, because it should be possible to get
further informations but not to change the space..
is there any schedule for publication the changed interface?
and maybe the same (space access) would be nice for view-selection too!?
thanks
Martin
Christian Schulte schrieb:
> Hmmm, I just had a look and it is not so obvious: would it be okay if
> the space would be passed as const? What does make more sense in this
> situation (I think const: because one should be able to ask the same
> space multiply for a description and always get the same description).
>
> Anyway, we are going to change the interface of branchings and
> branchin descriptions anyway: we decided to make branching
> descriptions mandatory given the huge importance they carry for
> efficient recomputation. This allows us to make the interfaces
> considerably simpler.
>
> Christian
>
> --
> Christian Schulte, http://www.imit.kth.se/~schulte/
>
> -----Original Message-----
> From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On
> Behalf Of Christian Schulte
> Sent: Tuesday, July 11, 2006 3:51 PM
> To: 'Martin Mann'; 'gecode user list'
> Subject: RE: [gecode-users] Space access during ViewSelection?
>
>
> That's okay. We will do that anytime soon.
>
> Christian
>
> --
> Christian Schulte, http://www.imit.kth.se/~schulte/
>
> -----Original Message-----
> From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On
> Behalf Of Martin Mann
> Sent: Tuesday, July 11, 2006 3:40 PM
> To: gecode user list
> Subject: [gecode-users] Space access during ViewSelection?
>
>
> ;)
>
> the next ugly question is waiting:
>
> This time I m fighting with value selection classes for my branching.
> In
> a function "int val(IntView &x)" the value to split on of the domain x
> is determined.
>
> I would like to have access to the "father" Space of x...
>
> The reason is the usage of some further informations out of Space, to
> select the value.
> Am I the only one who is interested in doing such things or may it be
> interesting for others too? If that's the case may it be possible to add
> the calling Space to the parameter list of "val(..)" in general? (I know
> this would lead to some changes in the code.. and to incompatibilities
> with user defined value selections for older versions of gecode..)
>
> Otherwise i ve got to do some static stuff I dont like, but that will
> do
> it for the moment.
>
> Thanks for thinking about
>
> Martin
>
> _______________________________________________
> Gecode users mailing list
> users at gecode.org https://www.gecode.org/mailman/listinfo/gecode-users
>
>
> _______________________________________________
> Gecode users mailing list
> users at gecode.org https://www.gecode.org/mailman/listinfo/gecode-users
>
>
_______________________________________________
Gecode users mailing list
users at gecode.org https://www.gecode.org/mailman/listinfo/gecode-users
More information about the gecode-users
mailing list


EE First
Get the first event in a set of event emitters and event pairs, then clean up after itself.
Install
$ npm install ee-first
API
var first = require('ee-first')
first(arr, listener)
Invoke listener
on the first event from the list specified in arr
. arr
is
an array of arrays, with each array in the format [ee, ...event]
. listener
will be called only once, the first time any of the given events are emitted. If
error
is one of the listened events, then if that fires first, the listener
will be given the err
argument.
The listener
is invoked as listener(err, ee, event, args)
, where err
is the
first argument emitted from an error
event, if applicable; ee
is the event
emitter that fired; event
is the string event name that fired; and args
is an
array of the arguments that were emitted on the event.
var ee1 = new EventEmitter()
var ee2 = new EventEmitter()
first([
[ee1, 'close', 'end', 'error'],
[ee2, 'error']
], function (err, ee, event, args) {
// listener invoked
})
.cancel()
The group of listeners can be cancelled before being invoked and have all the event listeners removed from the underlying event emitters.
var thunk = first([
[ee1, 'close', 'end', 'error'],
[ee2, 'error']
], function (err, ee, event, args) {
// listener invoked
})
// cancel and clean up
thunk.cancel()


Navigate with arrows or the space bar
Our future
One step at a time
A small question
The sitch. My husband and I planned a trip to Vegas. Though winning would be amazing - knowing that the slot machine odds weren't in our favor - I reframed the question.
"How can I lose as little as possible, while still having fun gambling?"
The challenge became measuring my performance. I planned how much I was willing to lose and recorded where and when I played, the amount spent, and the amount made. The one rule was I could never spend my winnings.
Findings
The strategy worked. We walked away with $196.28 in profit.
Set limits. Having pre-set limits mean that you weren't tricked into trying to win back your money. Sometimes you win, sometimes you lose, but both were in moderation.
Location matters. I discovered that the majority of money was spent and won in our hotel. This map provides insight into where we should stay next time we visit.
Key skills
A big question
The sitch. While I've always had a love-love relationship with math and numbers, I learned early on that this wasn't the case for everyone. Researchers estimate that 25 percent of college students have math anxiety. That's a lot of people in the work force who are avoiding math!
"How can I help adults improve their data literacy?"
Armed with a background in education, strong analytical skills, and lots of creativity, I began empowering adults to embrace data and its ability to inform decision-making.
Findings
Focus on real-life (practical) situations.While working as an instructional designer, I developed online educational materials that address common problems and make use of relevant data sets.
Behind the scenes + production-ready
Create conversation.Through conversation people can figure out how the new information they've learned relates/challenges/improves their current thinking and behavior. Conversation has the added bonus of encouraging relationship and community building (which have a whole host of their own benefits.
This PPT is an example of how data literacy training can be made interactive.
Integrate time for applying new skills.Whether adults are working online or in-person, they need to have
opportunities to practice the new skills they have learned.
Guides, training outlines, and other work samples are available upon request.
Key skills


| 1 |
Sawalha_2020 |
Epigenetic dysregulation of ACE2 and interferon-regulated genes might suggest increased COVID-19 susceptibility and severity in lupus patients |
Amr H Sawalha; Ming Zhao; Patrick Coit; Qianjin Lu |
2020 |
2020-04-04 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>47852 |
tdycrxtl |
0.731509 |
|
Sawalha_2020 |
| 2 |
Sawalha_2020 |
Epigenetic dysregulation of ACE2 and interferon-regulated genes might suggest increased COVID-19 susceptibility and severity in lupus patients |
Sawalha, Amr H.; Zhao, Ming; Coit, Patrick; Lu, Qianjin |
2020 |
2020-06-30 |
PMC |
Y |
|
|
10.1016/j.clim.2020.108410 |
xf4sofyc |
0.696014 |
|
Sawalha_2020 |
| 3 |
Haque_2009 |
The impact of surfactant protein-A on ozone-induced changes in the mouse bronchoalveolar lavage proteome |
Haque, Rizwanul; Umstead, Todd M; Freeman, Willard M; Floros, Joanna; Phelps, David S |
2009 |
2009-03-26 |
COMM-USE |
N |
PMC2666657 |
19323824.0 |
10.1186/1477-5956-7-12 |
tqfzfvsj |
0.662540 |
Choi_2020, Jang_2019 |
Drago-Serrano_2017 |
| 4 |
Surai_2019 |
Antioxidant Defence Systems and Oxidative Stress in Poultry Biology: An Update |
Surai, Peter F.; Kochish, Ivan I.; Fisinin, Vladimir I.; Kidd, Michael T. |
2019 |
2019-07-22 |
COMM-USE |
N |
PMC6680731 |
31336672.0 |
10.3390/antiox8070235 |
oj5v9d7p |
0.637814 |
Jang_2019, Choi_2020 |
Rehman_2018, Mocchegiani_2019, Mocchegiani_2008 |
| 5 |
Mocchegiani_2008 |
Role of Zinc and Selenium in Oxidative Stress and Immunosenescence: Implications for Healthy Ageing and Longevity |
Mocchegiani, Eugenio; Malavolta, Marco |
2008 |
2008-08-04 |
PMC |
N |
PMC7122608 |
|
10.1007/978-1-4020-9063-9_66 |
wh708hes |
0.604870 |
Jang_2019, Choi_2020, Cui_B_2019 |
Mocchegiani_2019, Drago-Serrano_2017, Arulselvan_2016 |
| 6 |
Kang_2005 |
2-Cys peroxiredoxin function in intracellular signal transduction: therapeutic implications |
Kang, Sang Won; Rhee, Sue Goo; Chang, Tong-Shin; Jeong, Woojin; Choi, Min Hee |
2005 |
2005-12-31 |
PMC |
N |
|
16290020.0 |
10.1016/j.molmed.2005.10.006 |
j9euxldq |
0.586867 |
Park_2016, Choi_2020 |
Schwertassek_2007, Stolf_2011 |
| 7 |
Mocchegiani_2019 |
Role of Zinc and Selenium in Oxidative Stress and Immunosenescence: Implications for Healthy Aging and Longevity |
Mocchegiani, Eugenio; Malavolta, Marco |
2019 |
2019-04-11 |
PMC |
N |
PMC7121636 |
|
10.1007/978-3-319-99375-1_66 |
o9qg2qta |
0.576284 |
Jang_2019, Rytkönen_2007 |
Mocchegiani_2008, Arulselvan_2016, Drago-Serrano_2017 |
| 8 |
Wiriyasermkul_2020 |
Membrane transport proteins in melanosomes: Regulation of ions for pigmentation |
Wiriyasermkul, Pattama; Moriyama, Satomi; Nagamori, Shushi |
2020 |
2020-04-22 |
PMC |
N |
|
|
10.1016/j.bbamem.2020.183318 |
9y9j45vs |
0.561755 |
Jang_2019 |
|
| 9 |
Chappell_2017 |
Variation in DNA-Damage Responses to an Inhalational Carcinogen (1,3-Butadiene) in Relation to Strain-Specific Differences in Chromatin Accessibility and Gene Transcription Profiles in C57BL/6J and CAST/EiJ Mice |
Chappell, Grace A.; Israel, Jennifer W.; Simon, Jeremy M.; Pott, Sebastian; Safi, Alexias; Eklund, Karl; Sexton, Kenneth G.; Bodnar, Wanda; Lieb, Jason D.; Crawford, Gregory E.; Rusyn, Ivan; Furey, Terrence S. |
2017 |
2017-10-16 |
PMC |
N |
PMC5944832 |
29038090.0 |
10.1289/ehp1937 |
bykx89fl |
0.556156 |
|
Jeon_2017 |
| 10 |
Murshid_2013 |
Stress Proteins in Aging and Life Span |
Murshid, Ayesha; Eguchi, Takanori; Calderwood, Stuart K. |
2013 |
2013-06-06 |
PMC |
N |
PMC4083487 |
23742046.0 |
10.3109/02656736.2013.798873 |
1i32cmhl |
0.532736 |
Choi_2020, Ohta_2011, Jang_2019 |
Hu_H_2019 |
| 11 |
Rehman_2018 |
Oxidative Stress in Poultry: Lessons from the Viral Infections |
Rehman, Zaib Ur; Meng, Chunchun; Sun, Yingjie; Safdar, Anum; Pasha, Riaz Hussain; Munir, Muhammad; Ding, Chan |
2018 |
2018-12-10 |
COMM-USE |
N |
PMC6311761 |
30647810.0 |
10.1155/2018/5123147 |
sop5vc4q |
0.496430 |
Lester_2014, Jang_2019 |
Drago-Serrano_2017 |
| 12 |
Kubota_2016 |
Epigenetic Effect of Environmental Factors on Autism Spectrum Disorders |
Kubota, Takeo; Mochizuki, Kazuki |
2016 |
2016-05-14 |
COMM-USE |
N |
PMC4881129 |
27187441.0 |
10.3390/ijerph13050504 |
rjggiklf |
0.443196 |
Rytkönen_2007 |
Mehta_2016, Trojano_2016 |
| 13 |
Yan_Z_2020 |
Extracellular superoxide dismutase, a molecular transducer of health benefits of exercise |
Yan, Zhen; Spaulding, Hannah R. |
2020 |
2020-03-19 |
None |
N |
PMC7109453 |
32220789.0 |
10.1016/j.redox.2020.101508 |
nv2ebnly |
0.420995 |
Choi_2020, Jang_2019, Cao_S_2015 |
Srikanthan_2016 |
| 14 |
Hanschmann_2013 |
Thioredoxins, Glutaredoxins, and Peroxiredoxins—Molecular Mechanisms and Health Significance: from Cofactors to Antioxidants to Redox Signaling |
Hanschmann, Eva-Maria; Godoy, José Rodrigo; Berndt, Carsten; Hudemann, Christoph; Lillig, Christopher Horst |
2013 |
2013-11-01 |
PMC |
N |
PMC3797455 |
23397885.0 |
10.1089/ars.2012.4599 |
dmn53j9d |
0.418855 |
Jang_2019, Rytkönen_2007, Levine_2015 |
Stolf_2011, Schwertassek_2007, Park_2016 |
| 15 |
Srikanthan_2016 |
The Role of Na/K-ATPase Signaling in Oxidative Stress Related to Obesity and Cardiovascular Disease |
Srikanthan, Krithika; Shapiro, Joseph I.; Sodhi, Komal |
2016 |
2016-09-03 |
COMM-USE |
N |
PMC5642908 |
27598118.0 |
10.3390/molecules21091172 |
qtobz75n |
0.409847 |
Jang_2019, Choi_2020, Levine_2015 |
Pratt_2019 |
| 16 |
Smith_2006 |
Demyelination: The Role of Reactive Oxygen and Nitrogen Species |
Smith, Kenneth J.; Kapoor, Raju; Felts, Paul A. |
2006 |
2006-04-05 |
PMC |
N |
PMC7161906 |
9989453.0 |
10.1111/j.1750-3639.1999.tb00212.x |
z3v89zsx |
0.399284 |
Choi_2020, Jang_2019, Ohl_K_2016 |
|
| 17 |
Szczawinska-Poplonyk_2020 |
A Novel CDC42 Mutation in an 11-Year Old Child Manifesting as Syndromic Immunodeficiency, Autoinflammation, Hemophagocytic Lymphohistiocytosis, and Malignancy: A Case Report |
Szczawinska-Poplonyk, Aleksandra; Ploski, Rafal; Bernatowska, Ewa; Pac, Malgorzata |
2020 |
2020-03-13 |
COMM-USE |
N |
PMC7082228 |
|
10.3389/fimmu.2020.00318 |
azcyu7ox |
0.382396 |
Levine_2015 |
|
| 18 |
Akaike_2001 |
Role of free radicals in viral pathogenesis and mutation |
Akaike, Takaaki |
2001 |
2001-03-16 |
PMC |
N |
PMC7169086 |
11262528.0 |
10.1002/rmv.303 |
6hb7icvm |
0.379975 |
Lester_2014 |
|
| 19 |
Pratt_2019 |
Mechanistic Insight of Na/K-ATPase Signaling and HO-1 into Models of Obesity and Nonalcoholic Steatohepatitis |
Pratt, Rebecca; Lakhani, Hari Vishal; Zehra, Mishghan; Desauguste, Rutmann; Pillai, Sneha S.; Sodhi, Komal |
2019 |
2019-12-21 |
COMM-USE |
N |
PMC6982200 |
31877680.0 |
10.3390/ijms21010087 |
aqmu9714 |
0.378700 |
Jang_2019, Choi_2020 |
Srikanthan_2016 |
| 20 |
Mocchegiani_2001 |
Zinc, infections and immunosenescence |
Mocchegiani, Eugenio; Giacconi, Robertina; Muzzioli, Mario; Cipriano, Catia |
2001 |
2001-01-20 |
PMC |
N |
PMC7126297 |
11164457.0 |
10.1016/s0047-6374(00)00194-9 |
pfcidkgp |
0.378186 |
Jang_2019, Lester_2014, Cui_B_2019 |
Drago-Serrano_2017, Mocchegiani_2008 |
| 21 |
Ghezzi_2011 |
Role of glutathione in immunity and inflammation in the lung |
Ghezzi, Pietro |
2011 |
2011-01-25 |
PMC |
N |
PMC3048347 |
21403800.0 |
10.2147/ijgm.s15618 |
ya2hidlu |
0.373584 |
Jang_2019, Lester_2014, Orvedahl_2008 |
Arulselvan_2016, Drago-Serrano_2017 |
| 22 |
Pennings_2018 |
Transcriptomics in lung tissue upon respiratory syncytial virus infection reveals aging as important modulator of immune activation and matrix maintenance |
Pennings, Jeroen L. A.; Mariman, Rob; Hodemaekers, Hennie M.; Reemers, Sylvia S. N.; Janssen, Riny; Guichelaar, Teun |
2018 |
2018-11-09 |
COMM-USE |
N |
PMC6226529 |
30413794.0 |
10.1038/s41598-018-35180-2 |
nkqjyl3v |
0.367917 |
Lester_2014 |
|
| 23 |
Checconi_2019 |
Role of Glutathionylation in Infection and Inflammation |
Checconi, Paola; Limongi, Dolores; Baldelli, Sara; Ciriolo, Maria Rosa; Nencioni, Lucia; Palamara, Anna Teresa |
2019 |
2019-08-20 |
COMM-USE |
N |
PMC6723385 |
31434242.0 |
10.3390/nu11081952 |
bg3b88us |
0.354776 |
Rytkönen_2007, Jang_2019 |
Drago-Serrano_2017, Stolf_2011 |
| 24 |
Erdős_2018 |
Large-scale analysis of redox-sensitive conditionally disordered protein regions reveal their widespread nature and key roles in high-level eukaryotic processes |
Gábor Erdős; Bálint Mészáros; Dana Reichmann; Zsuzsanna Dosztányi |
2018 |
2018-09-10 |
BioRxiv |
N |
|
|
10.1101/412692 |
99m0gt06 |
0.349928 |
Ivarsson_2012, Rytkönen_2007 |
|
| 25 |
Franklin_2006 |
Microbial Considerations in Genetically Engineered Mouse Research |
Franklin, Craig L. |
2006 |
2006-01-01 |
PMC |
N |
PMC7108594 |
16547371.0 |
10.1093/ilar.47.2.141 |
nd3tocwu |
0.349542 |
Orvedahl_2008 |
|
| 26 |
Lee_I_2016 |
Stress, Nutrition, and Intestinal Immune Responses in Pigs — A Review |
Lee, In Kyu; Kye, Yoon Chul; Kim, Girak; Kim, Han Wool; Gu, Min Jeong; Umboh, Johnny; Maaruf, Kartini; Kim, Sung Woo; Yun, Cheol-Heui |
2016 |
2016-05-12 |
NONCOMM |
N |
PMC4932560 |
27189643.0 |
10.5713/ajas.16.0118 |
j6cyu1id |
0.344742 |
Lester_2014, Hertzog_2011, Tiku_2020, Alcami_2016 |
Drago-Serrano_2017 |
| 27 |
Lucas_2013 |
Role of the Toll Like Receptor (TLR) Radical Cycle in Chronic Inflammation: Possible Treatments Targeting the TLR4 Pathway |
Lucas, Kurt; Maes, Michael |
2013 |
2013-02-26 |
PMC |
N |
PMC7091222 |
23436141.0 |
10.1007/s12035-013-8425-7 |
bnpzxwtc |
0.333853 |
Lester_2014, Geijtenbeek_2009 |
Arulselvan_2016 |
| 28 |
Nater_2009 |
Impact of acute psychosocial stress on peripheral blood gene expression pathways in healthy men |
Nater, Urs M.; Whistler, Toni; Lonergan, William; Mletzko, Tanja; Vernon, Suzanne D.; Heim, Christine |
2009 |
2009-10-31 |
PMC |
N |
PMC7116965 |
19577611.0 |
10.1016/j.biopsycho.2009.06.009 |
l7bhwg32 |
0.318567 |
Geijtenbeek_2009 |
|
| 29 |
Crowe_2016 |
Changes in the Transcriptome of Human Astrocytes Accompanying Oxidative Stress-Induced Senescence |
Crowe, Elizabeth P.; Tuzer, Ferit; Gregory, Brian D.; Donahue, Greg; Gosai, Sager J.; Cohen, Justin; Leung, Yuk Y.; Yetkin, Emre; Nativio, Raffaella; Wang, Li-San; Sell, Christian; Bonini, Nancy M.; Berger, Shelley L.; Johnson, F. Brad; Torres, Claudio |
2016 |
2016-08-31 |
COMM-USE |
N |
PMC5005348 |
27630559.0 |
10.3389/fnagi.2016.00208 |
lh2vanro |
0.318141 |
Jang_2019 |
Pennings_2018 |
| 30 |
Talotta_2019 |
Microbial Agents as Putative Inducers of B Cell Lymphoma in Sjögren's Syndrome through an Impaired Epigenetic Control: The State-of-The-Art |
Talotta, Rossella; Sarzi-Puttini, Piercarlo; Atzeni, Fabiola |
2019 |
2019-01-06 |
COMM-USE |
N |
PMC6339763 |
30723750.0 |
10.1155/2019/8567364 |
kg7c9a9h |
0.315394 |
Jang_2019 |
|
| 31 |
Jayapal_2006 |
Genome-wide gene expression profiling of human mast cells stimulated by IgE or FcεRI-aggregation reveals a complex network of genes involved in inflammatory responses |
Jayapal, Manikandan; Tay, Hwee Kee; Reghunathan, Renji; Zhi, Liang; Chow, Kah Kiong; Rauff, Mary; Melendez, Alirio J |
2006 |
2006-08-16 |
COMM-USE |
N |
PMC1564015 |
16911805.0 |
10.1186/1471-2164-7-210 |
il35gs97 |
0.308349 |
Bernardini_2012, Lester_2014 |
|
| 32 |
Dalgaard_2018 |
The influence of selenium and selenoproteins on immune responses of poultry and pigs |
Dalgaard, Tina S.; Briens, Mickaël; Engberg, Ricarda M.; Lauridsen, Charlotte |
2018 |
2018-04-30 |
PMC |
N |
|
|
10.1016/j.anifeedsci.2018.01.020 |
413m7czk |
0.306158 |
Lester_2014, Jang_2019, Orvedahl_2008 |
|


| CS 391 Selected Topics: Game AI Homework #5 |
Note: This work is to be done in groups of 2. Each group will submit one assignment. Although you may divide the work, both team members should be able to present/describe their partner's work upon request.
First:
Now consider a simplified form of Yahtzee consisting of only the 6 upper section categories ("ones" through "sixes") and the upper section bonus of 35 for a total of 63 or more. Let us call this minigame "Upper Section Yahtzee".
We will approach the computation of optimal Upper Section Yahtzee play in stages. In the first stage, you will count and construct the reachable Glenn-style "widgets" of play for all possible starts of turns. You need not at this stage fully implement the widgets for solving. It will be useful to store the widgets in such a way that will make it easy to retrieve them for retrograde analysis, i.e. solving turn-by-turn in stages from the last turn back to the beginning.
You will submit your code as a .zip file in an email that mentions the number of widgets generated for Upper Section Yahtzee.


{% extends "layout-with-search.html" %}
{% block page_title %}
Design patterns
{% endblock %}
{% import "includes/macros.html" as macro %}
{% block content %}
Follow this page
Design
Patterns
Content and navigation
Writing for services
Forms
Structuring forms
Pages
Page layout
User accounts and identity
Transactions
Can't find what you want?
Propose a new pattern
{% endblock %}


Hello world!
Click here to read the Tealeaves Coqdoc files
.
Here is a string diagram demonstrating the generalized notion of Kleisli composition formalized in Tealeaves. You can find the corresponding Coq definition
here in the docs
.


Guess
Hint: words are web related.
Follow Me on Codepen
Play Again?
{{expression.value}}
WRONG WORDS
{{expression}}
SKIP


require 'forwardable'
Rep is a small module to endow any class to make json quickly. It solves four problems:
attr_accessor
's that are prefilled from an options hash given to #initialize
The code is available on github.
Forwardable
is in the stdlib and allows ruby objects to delegate methods off to other objects. An example:
class A
extend Forwardable
delegate [:length, :first] => :@array
def initialize(array = [])
@array = array
end
end
A.new([1,2,3]).length # => 3
A.new([1,2,3]).first # => 1
require 'forwardable'
JSON::generate
and JSON::decode
are much safer to use than Object#to_json
.
require 'json'
require 'rep/version'
module Rep
All classes that include Rep
are extended with Forwardable
,
given some aliases, endowned with HashieSupport
if Hashie is loaded,
and given a delegate method if it doesn't already have one.
def self.included(klass)
klass.extend Forwardable
klass.extend ClassMethods
klass.instance_eval {
class << self
unless defined?(forward)
alias forward delegate
end
unless defined?(fields)
alias fields json_fields
end
end
if defined?(Mashed)
include MashedSupport
end
}
end
Since a goal is to be able to share instances, we need an easy way to reset a shared instance back to factory defaults. If you memoize any methods that are not declared as json fields, then overried this method and set any memoized variables to nil, then super.
def reset_for_json!
self.class.all_json_methods.each do |method_name|
instance_variable_set(:"@#{method_name}", nil)
end
end
All the work of generating a hash from an instance is packaged up in one method. Since
fields can be aliases in the format { :json_key_name => :method_name }
, there
is some fancy logic to determine the field_name
and method_name
variables.
{ :one => :foo }.to_a # => [[:one, :foo]]
Right now it will raise if either a field doesn't have a method to provide it's value or
if there are no json fields setup for the particular set (which defaults to :default
).
def to_hash(name = :default)
if fields = self.class.json_fields(name)
fields.each_with_object({}) do |field, memo|
field_name, method_name = field.is_a?(Hash) ? field.to_a.first : [field, field]
if self.respond_to?(method_name)
memo[field_name] = send(method_name)
else
message = "There is no method named '#{method_name}' for the class '#{self.class}' for the '#{name}' list of fields : #{e.message}"
raise NoMethodError.new(message, method_name, e.args)
end
end
else
raise "There are no json fields under the name: #{name}"
end
end
def to_mash(name = :default)
Mashed::Mash.new(to_hash(name))
end
def to_json
JSON.generate(to_hash)
end
module ClassMethods
Defines an attr_accessor with a default value. The default for default is nil. Example:
class A
register_accessor :name => "No Name"
end
A.new.name # => "No Name"
def register_accessor(acc)
name, default = acc.is_a?(Hash) ? acc.to_a.first : [acc, nil]
attr_accessor name
if default
define_method name do
var_name = :"@#{name}"
instance_variable_get(var_name) || instance_variable_set(var_name, default)
end
end
end
Defines an #initialize
method that accepts a Hash argument and copies some keys out into attr_accessors
.
If your class already has an #iniatialize
method then this will overwrite it (so don't use it). #initialize_with
does not have to be used to use any other parts of Rep.
def initialize_with(*args, &blk)
@initializiation_args = args
Remember what args we normally initialize with so we can refer to them when building shared instances.
if defined?(define_singleton_method)
define_singleton_method :initializiation_args do
@initializiation_args
end
else
singleton = class << self; self end
singleton.send :define_method, :initializiation_args, lambda { @initializiation_args }
end
Create an attr_accessor
for each one. Defaults can be provided using the Hash version { :arg => :default_value }
args.each { |a| register_accessor(a) }
define_method(:initialize) { |opts={}|
parse_opts(opts)
}
#parse_opts
is responsable for getting the attr_accessor
values prefilled. Since defaults can be specified, it
must negotiate Hashes and use the first key of the hash for the attr_accessor
's name.
define_method :parse_opts do |opts|
@rep_options = opts
blk.call(opts) unless blk.nil?
self.class.initializiation_args.each do |field|
name = field.is_a?(Hash) ? field.to_a.first.first : field
instance_variable_set(:"@#{name}", opts[name])
end
end
end
#json_fields
setups up some class instance variables to remember sets of top level keys for json structures. Example:
class A
json_fields [:one, :two, :three] => :default
end
A.json_fields(:default) # => [:one, :two, :three]
There is a general assumption that each top level key's value is provided by a method of the same name on an instance of the class. If this is not true, a Hash syntax can be used to alias to a different method name. Example:
class A
json_fields [{ :one => :the_real_one_method }, :two, { :three => :some_other_three }] => :default
end
Once can also set multiple sets of fields. Example:
class A
json_fields [:one, :two, :three] => :default
json_fields [:five, :two, :six] => :other
end
And all fields are returned by calling #json_fields
with no args. Example:
A.json_fields # => { :default => [:one, :two, :three], :other => [:five, :two, :six] }
def json_fields(arg = nil)
if arg.is_a?(Hash)
fields, name = arg.to_a.first
@json_fields ||= {}
@json_fields[name] = [fields].flatten
elsif arg.is_a?(Symbol)
@json_fields ||= {}
@json_fields[arg]
elsif arg === nil
@json_fields || {}
else
TODO: make an exception class
raise "You can only use a Hash to set fields, a Symbol to retrieve them, or no argument to retrieve all fields for all names"
end
end
#flat_json_fields
is just a utility method to DRY up the next two methods, because their code is almost exactly the same,
it is not intended for use directly and might be confusing.
def flat_json_fields(side = :right)
side_number = side == :right ? 1 : 0
json_fields.reduce([]) do |memo, (name, fields)|
memo + fields.map do |field|
if field.is_a?(Hash)
field.to_a.first[side_number] # [name, method_name]
else
field
end
end
end.uniq
end
We need a way to get a flat, uniq'ed list of all the fields accross all field sets. This is that.
def all_json_fields
flat_json_fields(:left)
end
We need a wya to get a flat, uniq'ed list of all the method names accross all field sets. This is that.
def all_json_methods
flat_json_fields(:right)
end
An easy way to save on GC is to use the same instance to turn an array of objects into hashes instead of instantiating a new object for every object in the array. Here is an example of it's usage:
class BookRep
initialize_with :book_model
fields :title => :default
forward :title => :book_model
end
BookRep.shared(:book_model => Book.first).to_hash # => { :title => "Moby Dick" }
BookRep.shared(:book_model => Book.last).to_hash # => { :title => "Lost Horizon" }
This should terrify you. If it doesn't, then this example will:
book1 = BookRep.shared(:book_model => Book.first)
book2 = BookRep.shared(:book_model => Book.last)
boo1.object_id === book2.object_id # => true
It really is a shared object.
You really shouldn't use this method directly for anything.
def shared(opts = {})
@pointer = (Thread.current[:rep_shared_instances] ||= {})
@pointer[object_id] ||= new
@pointer[object_id].reset_for_json!
@pointer[object_id].parse_opts(opts)
@pointer[object_id]
end
The fanciest thing in this entire library is this #to_proc
method. Here is an example of it's usage:
class BookRep
initialize_with :book_model
fields :title => :default
forward :title => :book_model
end
Book.all.map(&BookRep) # => [{ :title => "Moby Dick" }, { :title => "Lost Horizon " }]
And now I will explain how it works. Any object can have a to_proc method and when you call #map
on an
array and hand it a proc it will in turn hand each object as an argument to that proc. What I've decided
to do with this object is use it the options for a shared instance to make a hash.
Since I know the different initialization argumants from a call to initialize_with
, I can infer by order
which object is which option. Then I can create a Hash to give to parse_opts
through the shared
method.
I hope that makes sense.
It allows for extremely clean Rails controllers like this:
class PhotosController < ApplicationController
respond_to :json, :html
def index
@photos = Photo.paginate(page: params[:page], per_page: 20)
respond_with @photos.map(&PhotoRep)
end
def show
@photo = Photo.find(params[:id])
respond_with PhotoRep.new(photo: @photo)
end
end
def to_proc
proc { |obj|
arr = [obj].flatten
init_args = @initializiation_args[0..(arr.length-1)]
opts = Hash[init_args.zip(arr)]
shared(opts).to_hash
}
end
end
end


{% if search_project %}
{% with "project:"|add:search_project as search_term %}
{% include "core/widesearchbar.html" with form_action="//"|add:PRODUCTION_DOMAIN|add:"/search/" term=search_term %}
{% endwith %}
{% else %}
{% include "core/widesearchbar.html" with form_action="//"|add:PRODUCTION_DOMAIN|add:"/search/" %}
{% endif %}