A solutions company that solves problems using software.
Scroll to view our process.
At Unabridged Software, we start with a conversation that allows us to get to the root of your issue. We’ll ask questions, understand your vantage point and see where we’ll go next with the project. Can we fix it? Absolutely.
Solid solutions require solid plans. We brainstorm together to figure out the best approach to fixing, creating and improving your software environment.
We like to think of our productions as ‘continuous collaborations’ between you and us. Working in tandem, we code, test, refine and work in sprints. We’ll keep you in the loop with weekly updates so you know what we’re up to.
This is where that conversation becomes a working solution. We’ll send the digital files and help you implement them into your current system. We’ll listen to your feedback, fine-tune where it’s needed and get you acclimated to your new environment.
Here are some of the great solutions we’ve created in the past. Have a look around.
Why don’t we start that conversation today?
Send us an email, give us a ring or talk to us via Skype.
Nick grew up in Omaha. He graduated from the University of Nebraska with B.S. in Computer Science and Mathematics. He loves to travel and has visited Uruguay, Argentina and Mexico. He enjoys espresso and his hobbies include playing golf and watching Nebraska football. He is married to the amazing Jessi Reiss and they have a Boston Terrier, Winston. His favorite beer is Goose Island Matilda.
402.522.6686
<EMAIL_ADDRESS>Drew grew up in South Dakota and graduated from the University of Nebraska with a Bachelor of Science in Computer Science. He contributes to major Ruby open-source projects and spends most of his free time thinking about work. He lives in Lincoln with his wonderful wife, Lisa and their daughter, Estella.


It is possible to protect a part of a program against abrupt termination in the event of an exception. There are two ways to do this:
on_exception/3
user:unknown_predicate_handler/3


2 writes to Culture
System.Management.Automation (2)
engine\remoting\commands\NewPSSessionOptionCommand.cs (1)
412
result.
Culture
= this.Culture;
engine\serialization.cs (1)
7052
option.
Culture
= GetPropertyValue<CultureInfo>(pso, "Culture");
2 references to Culture
System.Management.Automation (2)
engine\remoting\common\RunspaceConnectionInfo.cs (2)
290
if (options.
Culture
!= null)
292
this.Culture = options.
Culture
;


When several users or teams share a cluster with a fixed number of nodes, there is a concern that one team could use more than its fair share of resources.
Resource quotas are a tool for administrators to address this concern. Resource quotas work like this:
403 FORBIDDEN
.403 FORBIDDEN
. Hint: Use the LimitRange admission controller to force default
values of limits (then resource requests would be equal to limits by default, see
admission controller) before the quota is checked to avoid this problem.Examples of policies that could be created using namespaces and quotas are:
In the case where the total capacity of the cluster is less than the sum of the quotas of the namespaces, there may be contention for resources. This is handled on a first-come-first-served basis.
Neither contention nor changes to quota will affect already-running pods.
Resource Quota support is enabled by default for many Kubernetes distributions. It is
enabled when the apiserver --admission-control=
flag has ResourceQuota
as
one of its arguments.
Resource Quota is enforced in a particular namespace when there is a
ResourceQuota
object in that namespace. There should be at most one
ResourceQuota
object in a namespace.
The total sum of compute resources requested by pods in a namespace can be limited. The following compute resource types are supported:
| ResourceName | Description |
|---|---|
| cpu | Total cpu requests of containers |
| memory | Total memory requests of containers |
For example, cpu
quota sums up the resources.requests.cpu
fields of every
container of every pod in the namespace, and enforces a maximum on that sum.
The number of objects of a given type can be restricted. The following types are supported:
| ResourceName | Description |
|---|---|
| pods | Total number of pods |
| services | Total number of services |
| replicationcontrollers | Total number of replication controllers |
| resourcequotas | Total number of resource quotas |
| secrets | Total number of secrets |
| persistentvolumeclaims | Total number of persistent volume claims |
For example, pods
quota counts and enforces a maximum on the number of pods
created in a single namespace.
You might want to set a pods quota on a namespace to avoid the case where a user creates many small pods and exhausts the cluster’s supply of Pod IPs.
Kubectl supports creating, updating, and viewing quotas:
$ kubectl namespace myspace
$ cat <<EOF > quota.json
{
"apiVersion": "v1",
"kind": "ResourceQuota",
"metadata": {
"name": "quota",
},
"spec": {
"hard": {
"memory": "1Gi",
"cpu": "20",
"pods": "10",
"services": "5",
"replicationcontrollers":"20",
"resourcequotas":"1",
},
}
}
EOF
$ kubectl create -f ./quota.json
$ kubectl get quota
NAME
quota
$ kubectl describe quota quota
Name: quota
Resource Used Hard
-------- ---- ----
cpu 0m 20
memory 0 1Gi
pods 5 10
replicationcontrollers 5 20
resourcequotas 1 1
services 3 5
Resource Quota objects are independent of the Cluster Capacity. They are expressed in absolute units. So, if you add nodes to your cluster, this does not automatically give each namespace the ability to consume more resources.
Sometimes more complex policies may be desired, such as:
Such policies could be implemented using ResourceQuota as a building-block, by writing a ‘controller’ which watches the quota usage and adjusts the quota hard limits of each namespace according to other signals.
Note that resource quota divides up aggregate cluster resources, but it creates no restrictions around nodes: pods from several namespaces may run on the same node.
See a detailed example for how to use resource quota.
See ResourceQuota design doc for more information.


Towards understanding situations in videos
VidSitu is a large-scale dataset containing diverse 10-second videos from movies depicting complex situations (a collection of related events). Events in the video are richly annotated at 2-second intervals with verbs, semantic-roles, entity co-references, and event relations.
Annotations in VidSitu support the Video Semantic Role Labeling (VidSRL) task which consists of 3 subtasks.
If you find our work helpful, please cite the following paper:
@InProceedings{Sadhu_2021_CVPR,
author = {Sadhu, Arka and Gupta, Tanmay and Yatskar, Mark and Nevatia, Ram and Kembhavi, Aniruddha},
title = {Visual Semantic Role Labeling for Video Understanding},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021}}
Detailed instructions for downloading VidSitu are provided on the accompanying Github repo. This repo provides:
Please reach out to Arka Sadhu<EMAIL_ADDRESS>for any queries.


⬆
HXL toolchain cookbook
[Recipe title]
View recipe in the HXL Proxy.
[Opening]
Province
Cluster
#adm1
#sector
Coast Province
WASH
Plains Province
WASH
Mountains Province
WASH
[Step instructions]
[Param name]
[Param value]
Result:


Testing jMenu
jMenu allow user to create context menu or normal menu.
Context menu
click anywhere on this block to display sample context menu
testing something
testing something
testing something
testing something
testing something
testing something
testing something
a
b
c


class QuoteParser
(source)
A parser to separate a quote from useful email content.
To instantiate this class use QuoteParser.Builder. Typical usage: create QuoteParser instance with builder and call its parse() method either with file or list of strings.
| Builder |
class Builder
Builder for QuoteParser class. |
| parse |
fun parse(emlFile: File): Content
Parse the given file to separate a quote from the useful content.
fun parse(lines: List<String>, hasInReplyToEMLHeader: Boolean = true): Content
Parse given list of strings to separate a quote from the useful content. |


Which timebox and habits techniques did you try?
I have never meditated before, so I gave that a try. I also tried the Tiny Habits method and the Pomodoro technique.
What did you find interesting?
I found the Tiny Habits method the most interesting, as over time I think it could create really positive and effective changes in peoples lives.
What worked for you?
A combination of the Pomodoro technique and the Tiny Habits method.
Which will you experiment further with?
I would definitely like to give meditation another shot.


| Class and Description |
|---|
| Activity
An activity is the link between a processor and the
actual service.
|
| AnnotatedGranularDepthPorts
List of annotated ports that have both depth and
granular depth.
|
| AnnotatedPorts
List of annotated ports.
|
| Annotations
List of annotation chains
|
| ConfigBean
Java class for ConfigBean complex type.
|
| Dataflow
A dataflow, that is a workflow that is data-driven.
|
| Datalinks
List of datalinks between dataflow ports and
processors in a workflow.
|
| DepthPorts
List of ports that have depths.
|
| DispatchLayer
A dispatch layer, identified by Java class name.
|
| DispatchStack
List of dispatch layers.
|
| GranularDepthPorts
List of ports that have both depth and granular depth.
|
| IterationNode
Java class for IterationNode complex type.
|
| IterationStrategyStack
Java class for IterationStrategyStack complex type.
|
| Link
One end of the links in the Datalink.
|
| Map
0 or more mappings.
|
| Processors
List of processors.
|
| Workflow
The container of a workflow definition.
|
| Class and Description |
|---|
| ActivityPortDefinitionBean
Java class for ActivityPortDefinitionBean complex type.
|
| ConfigBean
Java class for ConfigBean complex type.
|
| Class and Description |
|---|
| Activity
An activity is the link between a processor and the
actual service.
|
| ConfigBean
Java class for ConfigBean complex type.
|
| Class and Description |
|---|
| ConfigBean
Java class for ConfigBean complex type.
|
| UsecaseDescription
Java class for UsecaseDescription complex type.
|
| Class and Description |
|---|
| AbstractConfigBean
Java class for AbstractConfigBean complex type.
|
| AbstractTextual
Java class for AbstractTextual complex type.
|
| Activities
List of activities.
|
| Activity
An activity is the link between a processor and the
actual service.
|
| ActivityInputPorts
Java class for ActivityInputPorts complex type.
|
| ActivityInputs
Java class for ActivityInputs complex type.
|
| ActivityInputs.Entry
Java class for anonymous complex type.
|
| ActivityOutputPorts
Java class for ActivityOutputPorts complex type.
|
| ActivityPortDefinitionBean
Java class for ActivityPortDefinitionBean complex type.
|
| ActivityPortDefinitionBean.MimeTypes
Java class for anonymous complex type.
|
| AnnotatedGranularDepthPort
Java class for AnnotatedGranularDepthPort complex type.
|
| AnnotatedGranularDepthPorts
List of annotated ports that have both depth and
granular depth.
|
| AnnotatedPort
Java class for AnnotatedPort complex type.
|
| AnnotatedPorts
List of annotated ports.
|
| AnnotationAssertionImpl
Java class for AnnotationAssertionImpl complex type.
|
| AnnotationAssertionImpl.NetSfTavernaT2AnnotationAnnotationAssertionImpl
Java class for anonymous complex type.
|
| AnnotationBean
Java class for AnnotationBean complex type.
|
| AnnotationChain
Chain of annotations.
|
| AnnotationChain.NetSfTavernaT2AnnotationAnnotationChainImpl
Java class for anonymous complex type.
|
| Annotations
List of annotation chains
|
| ApiConsumerConfig
Java class for ApiConsumerConfig complex type.
|
| ApiConsumerParameterDimensions
Java class for ApiConsumerParameterDimensions complex type.
|
| ApiConsumerParameterNames
Java class for ApiConsumerParameterNames complex type.
|
| ApiConsumerParameterTypes
Java class for ApiConsumerParameterTypes complex type.
|
| ArtifactDependencies
Java class for ArtifactDependencies complex type.
|
| Author
Java class for Author complex type.
|
| BasicArtifact
Java class for BasicArtifact complex type.
|
| BeanshellConfig
Configuration for the Beanshell activity.
|
| BioMartConfig
Java class for BioMartConfig complex type.
|
| BioMartConfig.MartService
Java class for anonymous complex type.
|
| BioMobyCategory
Java class for BioMobyCategory complex type.
|
| BioMobyConfig
Java class for BioMobyConfig complex type.
|
| BioMobyEntry
Java class for BioMobyEntry complex type.
|
| BioMobyObjectConfig
Java class for BioMobyObjectConfig complex type.
|
| BioMobyParserConfig
Java class for BioMobyParserConfig complex type.
|
| BioMobySecondaries
Java class for BioMobySecondaries complex type.
|
| BioMobyServiceType
Java class for BioMobyServiceType complex type.
|
| ClassLoaderSharing
Java class for ClassLoaderSharing.
|
| ComponentConfig
Java class for ComponentConfig complex type.
|
| Condition
A conditional link.
|
| Conditions
List of conditional control links.
|
| ConfigBean
Java class for ConfigBean complex type.
|
| CrossProduct
Java class for CrossProduct complex type.
|
| Dataflow
A dataflow, that is a workflow that is data-driven.
|
| DataflowConfig
Configuration for the nested dataflow activity.
|
| DataLink
A link between two dataflow units, from source to
link.
|
| Datalinks
List of datalinks between dataflow ports and
processors in a workflow.
|
| Dependencies
Java class for Dependencies complex type.
|
| DependencyElement
Java class for DependencyElement complex type.
|
| DepthPort
Java class for DepthPort complex type.
|
| DepthPorts
List of ports that have depths.
|
| DescriptiveTitle
Java class for DescriptiveTitle complex type.
|
| DispatchLayer
A dispatch layer, identified by Java class name.
|
| DispatchStack
List of dispatch layers.
|
| DotProduct
Java class for DotProduct complex type.
|
| Encoding
Java class for Encoding.
|
| Entry
Java class for Entry complex type.
|
| EntryMap
Java class for EntryMap complex type.
|
| ExampleValue
Java class for ExampleValue complex type.
|
| ExternalToolConfig
Java class for ExternalToolConfig complex type.
|
| FreeTextDescription
Java class for FreeTextDescription complex type.
|
| GranularDepthPort
Java class for GranularDepthPort complex type.
|
| GranularDepthPorts
List of ports that have both depth and granular depth.
|
| Group
Java class for Group complex type.
|
| HostInstitution
Java class for HostInstitution complex type.
|
| HTTPHeaders
Java class for HTTPHeaders complex type.
|
| HTTPHeaders.List
Java class for anonymous complex type.
|
| IdentificationAssertion
Java class for IdentificationAssertion complex type.
|
| InteractionConfig
Java class for InteractionConfig complex type.
|
| Iteration
Java class for Iteration complex type.
|
| IterationNode
Java class for IterationNode complex type.
|
| IterationNodeParent
Java class for IterationNodeParent complex type.
|
| IterationStrategyStack
Java class for IterationStrategyStack complex type.
|
| LastPredictedDepthPort
Java class for LastPredictedDepthPort complex type.
|
| Link
One end of the links in the Datalink.
|
| LinkType
Java class for LinkType.
|
| LocalDependencies
Java class for LocalDependencies complex type.
|
| LoopConfig
Configuration of the loop layer,
which can call an activity again on a given condition.
|
| Map
0 or more mappings.
|
| Mapping
A mapping of port names.
|
| MimeType
Java class for MimeType complex type.
|
| Optional
Java class for Optional complex type.
|
| OriginalConfigBean
A serialised configuration bean to configure a
dispatch layer or activity.
|
| OutputFormat
Java class for OutputFormat.
|
| ParallelizeConfig
Defines maximum number of jobs to run
concurrently.
|
| Port
Java class for Port complex type.
|
| PortProduct
Java class for PortProduct complex type.
|
| Ports
List of ports.
|
| Processor
A processor of a dataflow.
|
| Processors
List of processors.
|
| Properties
Java class for Properties complex type.
|
| Property
Java class for Property complex type.
|
| Raven
Raven artifact described using typical Maven groupId,
artifactId and version.
|
| RESTConfig
Java class for RESTConfig complex type.
|
| RetryConfig
Defines parameters of retries for when an
activity fails.
|
| ReturnCodes
Java class for ReturnCodes complex type.
|
| Role
Java class for role.
|
| RShellConfig
Java class for RShellConfig complex type.
|
| RShellConnection
Java class for RShellConnection complex type.
|
| RShellSymanticType
Java class for RShellSymanticType complex type.
|
| RShellSymanticType.RShellPortSymanticTypeBean
Java class for anonymous complex type.
|
| RShellSymanticType.RShellPortSymanticTypeBean.SymanticType
Java class for anonymous complex type.
|
| ScriptInputStatic
Java class for ScriptInputStatic complex type.
|
| ScriptInputStatic.Content
Java class for anonymous complex type.
|
| ScriptInputUser
Java class for ScriptInputUser complex type.
|
| ScriptOutput
Java class for ScriptOutput complex type.
|
| SemanticAnnotation
Java class for SemanticAnnotation complex type.
|
| SoaplabConfig
Configuration for the SoapLab activity.
|
| SpreadsheetColumnNameEntry
Java class for SpreadsheetColumnNameEntry complex type.
|
| SpreadsheetColumnNames
Java class for SpreadsheetColumnNames complex type.
|
| SpreadsheetEmptyCellPolicy
Java class for SpreadsheetEmptyCellPolicy.
|
| SpreadsheetExcludes
Java class for SpreadsheetExcludes complex type.
|
| SpreadsheetImportConfig
Java class for SpreadsheetImportConfig complex type.
|
| SpreadsheetRange
Java class for SpreadsheetRange complex type.
|
| StandardWSDLSecurityProfile
Java class for StandardWSDLSecurityProfile.
|
| StaticInputs
Java class for StaticInputs complex type.
|
| StringConstantConfig
Java class for StringConstantConfig complex type.
|
| TopIterationNode
Java class for TopIterationNode complex type.
|
| UsecaseConfig
Java class for UsecaseConfig complex type.
|
| UsecaseDescription
Java class for UsecaseDescription complex type.
|
| Workflow
The container of a workflow definition.
|
| WSDLConfig
Configuration for the WSDL activity.
|
| XMLSplitterConfig
Java class for XMLSplitterConfig complex type.
|
| XPathConfig
Java class for XPathConfig complex type.
|
| XPathNamespaceMap
Java class for XPathNamespaceMap complex type.
|
| XPathNamespaceMap.Entry
Java class for anonymous complex type.
|
Copyright © 2015–2016 The Apache Software Foundation. All rights reserved.


Big sports events today
by TIMESOFINDIA.COMNEW DELHI: Here are the big sports events lined up for Wednesday (November 20) that comprise action from Korea Masters badminton and ISSF World Cup Final.
(All timings in IST)
1:15 am (Football - European Championship Qualifiers)
Germany vs Northern Ireland (Germany won 6-1)
Netherlands vs Estonia (Netherlands won 5-0)
Slovakia vs Azerbaijan (Slovakia won 2-0)
Wales vs Hungary (Wales won 2-0)
North Macedonia vs Israel (North Macedonia won 1-0)
Latvia vs Austria (Latvia won 1-0)
Poland vs Slovenia (Poland won 3-2)
Belgium vs Cyprus (Belgium won 6-1)
San Marino vs Russia (Russia won 5-0)
Scotland vs Kazakhstan (Scotland won 3-1)
1:15 am (Football - FA Cup)
Bradford City vs Shrewsbury Town (Shrewsbury won 1-0)
Bromley vs Bristol Rovers (Bristol won 1-0)
Burton Albion vs Salford City (Burton won 4-1)
Doncaster Rovers vs AFC Wimbledon (Doncaster won 2-0)
Eastleigh vs Stourbridge (Eastleigh won 3-0)
Exeter City vs Cambridge United (Exeter won 1-0)
Gillingham vs Sunderland (Gillingham won 1-0)
Peterborough United vs Stevenage (Peterborough won 2-0)
Rochdale vs Wrexham (Rochdale won 1-0)
Swindon Town vs Cheltenham Town (Cheltenham won 1-0)
6:10 am (Badminton - Korea Masters)
Sameer Verma vs Kazumasa Sakai of Japan in men's singles first round (Sameer won after Kazumasa retired hurt trailing 8-11 in the first game)
6:30 am (Football - International friendly)
Ecuador vs Colombia (Colombia won 1-0)
6:30 am (Shooting - ISSF World Cup Final Rifle/Pistol)
India's Rahi Sarnobat, Manu Bhaker in 25m Pistol Women Qualification (Manu finished 10th and Rahi 18th in qualification with scores of 583 and 569, respectively)
7:00 am (Badminton - Korea Masters)
Sourabh Verma vs Kim Donghun of Korea in men's singles first round (Sourabh lost 21-13, 12-21, 13-21)
7:30 am (Badminton - Korea Masters)
Kidambi Srikanth vs Wong Wing Ki Vincent of Hong Kong China in men's singles first round (Srikanth won 21-18, 21-17)
8:30 am (Cricket - ACC Emerging Teams Asia Cup, 1st semifinal)
Pakistan U23 vs India U23
9:00 am (Shooting - ISSF World Cup Final Rifle/Pistol)
India's Anish Bhanwala in 25m Rapid Fire Pistol Men Qualification


I have experience in openGL creating a platform to load and render 3d models.
Experienced in developing games using PyGame and creating bespoke tools.
Using Javascript I created a small game called “BoxBall” which can be played on this website.
Created a MUD (Multi-User Dungeon) which involved client/server networking.
I have worked with Unreal for 3 years and developed multiple games working in both VR and mobile.
Knowledgeable on how to use GitHub to manage programming projects.
Unity Certified Developer!
Have worked with HTML to create websites such as this one.


Due to the disappointing attempt at simulating air lift on a wing, I have decided to test the flow simulator on something simpler.
I chose to see if the flowsim was capable of simulating effects on the medium(water) due to a mechanical movment of objets in the medium.
Unfortunately, the flowsim was limited to rotating objects, and I had to choose to test it on a rotor, similar to the ones used for boats.
Additoinally, I found that the flowsim doesn't actually rotate the object, but rather asks the user to select a region of medium that will rotate around the object. I am not sure the reason for this approach, but I guess it was to simplify the calculations needed for the analysis.
Below is the image of the rotor I drafted out for this test. I have no experience in rotor design nor do I know how it works. Therefore, all I could do was make something that resembles a rotor.
As mentioned above, after the initial set up I must indicate a region in which the medium will rotate around the object. This is done by encasing the object in question with a cylinder.
I drew a cylinder around the rotor and made it transparent as shown below.
After inserting the region into the flowsim tree, and setting the radians per second to 90 (about 860 rpm), I defined the goals to be the Y direction force and the torque around the Y-axis.
Below is the video of the resulting flow trajectories:
The Y force converged to be about 153 Newtons. This force would be used to push the boat for example.
The Y axis torque value converged to be about 7.25 N*m. This would be the amount of load that the motor spinning the rotor will have to overcome.
If my goal was to design the best rotor, I would tweak the design of the rotor until I get the highest Y force to Y axis torque ratio.
One thing to note is that the above calculation was done without the "cavitation" option enabled.
For those who are unfamiliar with the concept of cavitation here is a fun video explaining the basic concept of cavitation: Cavitation due to AK4
The calculation with the cavitation option enabled took about 35 minutes as opposed to 2 minutes without the cavitation option.
The results also differed. The Y direction force converged to about 105 Newtons, and the Y-axis torque converged to about 6 Newton meters. Both of these values are significantly lower than the ones given by the calculation without the cavitation, and it leads me to believe that these values may be closer to the reality.
As for the visual trajectories of the calculations, they both were near identical.
As far as testing this feature goes, it is a success. The Solidworks flowsimulatior seems to be useful in rotor design. Of course I can't be sure of its accuracy unless I have some proven standards to compare the result to.


| Up | TOC | Index | |||||
| << 3 Using Euphoria | < 4.6 Flow control statements | Up: 4 Language Reference | 4.8 Special Top-Level Statements > | 5 Formal Syntax >> |
4.7 Short-Circuit Evaluation
When the condition tested by if, elsif, until, or while contains and or or operators, short_circuit evaluation will be used. For example,
if a < 0 and b > 0 then ...
If a < 0 is false, then Euphoria will not bother to test if b is greater than 0. It will know that the overall result is false regardless. Similarly,
if a < 0 or b > 0 then ...
if a < 0 is true, then Euphoria will immediately decide that the result is true, without testing the value of b, since the result of this test would be irrelevant.
In general, whenever we have a condition of the form:
A and B
where A and B can be any two expressions, Euphoria will take a short-cut when A is false and immediately make the overall result false, without even looking at expression B.
Similarly, with:
A or B
when A is true, Euphoria will skip the evaluation of expression B, and declare the result to be true.
If the expression B contains a call to a function, and that function has possible side-effects, i.e. it might do more than just return a value, you will get a compile-time warning. Older versions (pre-2.1) of Euphoria did not use short_circuit evaluation, and it's possible that some old code will no longer work correctly, although a search of the Euphoria archives did not turn up any programs that depend on side-effects in this way, but other Euphoria code might do so.
The expression, B, could contain something that would normally cause a run-time error. If Euphoria skips the evaluation of B, the error will not be discovered. For instance:
if x != 0 and 1/x > 10 then -- divide by zero error avoided while 1 or {1,2,3,4,5} do -- illegal sequence result avoided
B could even contain uninitialized variables, out-of-bounds subscripts etc.
This may look like sloppy coding, but in fact it often allows you to write something in a simpler and more readable way. For instance:
if length(x) > 1 and x[2] = y then
Without short-circuiting, you would have a problem when x contains less than 2 items. With short-circuiting, the assignment to x[2] will only be done when x has at least 2 items. Similarly:
-- find 'a' or 'A' in s i = 1 while i <= length(s) and s[i] != 'a' and s[i] != 'A' do i += 1 end while
In this loop the variable i might eventually become greater than length(s). Without short-circuit evaluation, a subscript out-of-bounds error will occur when s[i] is evaluated on the final iteration. With short-circuiting, the loop will terminate immediately when i <= length(s) becomes false. Euphoria will not evaluate s[i] != 'a' and will not evaluate s[i] != 'A'. No subscript error will occur.
Short-circuit evaluation of and and or takes place inside decision making expressions. These are found in the if statement, while statement and the loop until statement. It is not used in other contexts. For example, the assignment statement:
x = 1 or {1,2,3,4,5} -- x should be set to {1,1,1,1,1}
If short-circuiting were used here, we would set x to 1, and not even look at {1,2,3,4,5}. This would be wrong. Short-circuiting can be used in if/elsif/until/while conditions because we only care if the result is true or false, and conditions are required to produce an atom as a result.


Webinar on Study Abroad
A webinar on exams and scholarships to pursue higher studies outside India.
Growth Hacks for Linkedin
A webinar on networking on linkedin from industry experts
Workshop on GSoC | Git GitHub | Open Source
A webinar on introduction to GSoC, Git, Github and Opensource


Name:
Email:
Phone:
Location:
Item:
For Sale
Wanted
For Trade
Other
Price:
Firm
OBO
Cash or money order only
Description:


--- layout: default --- {% capture badges %}{% include badge.md %}{% endcapture %} {{ badges | markdownify }}
This component is available on GitHub.


Alain
Java Intermediate, Python Beginner, Machine Learning Fan
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Bloom Filter
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LeetCode | Spiral Matrix in Java
LeetCode | LRU Cache in Java
LeetCode | Permutations in Java
LeetCode | N-Queens II in Java
LeetCode | Best Time to Buy and Sell Stock in Java
LeetCode | Letter Combinations of a Phone Number in Java
LeetCode | Convert Sorted List to Binary Search Tree in Java
LeetCode | Candy in Java
LeetCode | Combinations in Java
HashMap Structure
LeetCode | Same Tree in Java
Max(Priority) Heap in Java and Python
LeetCode | Symmetric Tree in Java
Stack vs LinkedList vs ArrayDeque
LeetCode | Evaluate Reverse Polish Notation in Java
Vector vs SynchronizedList
Vector vs ArrayList
Max-Min HashMap
Singleton
Decorator
Java Source Code - Integer
Java Source Code - Arrays
Pass Parameters to A Function
Hash Function
Machine_Learning
Newton's Method
BaseEstimator in Scikit-Learn
Python
Find Path at Binary Tree
LeetCode | Validate Binary Search Tree in Python
LeetCode | Implement strStr() in Python
LeetCode | Restore IP Addresses in Python
LeetCode | 3Sum Closest in Python
Max(Priority) Heap in Java and Python
LeetCode | Largest Rectangle in Histogram in Python
LeetCode | Valid Parentheses in Python
LeetCode | Reverse Nodes in k-Group in Python
LeetCode | Wildcard Matching in Python
LeetCode | Valid Number in Python
LeetCode | Surrounded Regions in Python
LeetCode | Regular Expression Matching in Python
LeetCode | Unique Binary Search Trees II in Python
LeetCode | Reverse Words in a String in Python
LeetCode | Remove Element in Python
LeetCode | Evaluate Reverse Polish Notation in Python
LeetCode | Search in Rotated Sorted Array II in Python
LeetCode | Search in Rotated Sorted Array in Python
LeetCode | Maximum Product Subarray in Python
LeetCode | Longest Substring Without Repeating Characters in Python
LeetCode | Letter Combinations of a Phone Number in Python
LeetCode | Word Ladder in Python
LeetCode | Sqrt(x) in Python
LeetCode | Reverse Integer in Python
LeetCode | Recover Binary Search Tree in Python
LeetCode | Merge k Sorted Lists in Python
LeetCode | Maximum Depth of Binary Tree in Python
LeetCode | LRU Cache in Python
LeetCode | Copy List with Random Pointer in Python
LeetCode | Path Sum II in Python
LeetCode | Path Sum in Python
LeetCode | Minimum Depth of Binary Tree in Python
LeetCode | Sort Colors in Python
LeetCode | Single Number in Python
LeetCode | Same Tree in Python
LeetCode | Binary Tree Maximum Path Sum in Python
LeetCode | Merge Sorted Array in Python
LeetCode | Max Points on a Line in Python
LeetCode | Clone Graph in Python
LeetCode | Climbing Stairs in Python
LeetCode | Binary Tree Maximum Path Sum in Python
LeetCode | Two Sum in Python
LeetCode | Remove Nth Node From End of List in Python
LeetCode | Next Permutation in Python
LeetCode | Merge Two Sorted Lists in Python
LeetCode | Jump Game II in Python
LeetCode | Jump Game in Python
LeetCode | Generate Parentheses in Python
LeetCode | Count and Say in Python
LeetCode | ZigZag Conversion in Python
LeetCode | Word Search in Python
LeetCode | Valid Sudoku in Python
LeetCode | Valid Palindrome in Python
LeetCode | Unique Binary Search Trees in Python
LeetCode | Triangle in Python
LeetCode | Trapping Rain Water in Python
LeetCode | Symmetric Tree in Python
LeetCode | Swap Nodes in Pairs in Python
LeetCode | Substring with Concatenation of All Words in Python
LeetCode | String to Integer (atoi) in Python
LeetCode | Sort List in Python
LeetCode | Simplify Path in Python
LeetCode | Search Insert Position in Python
LeetCode | Search for a Range in Python
LeetCode | Remove Duplicates from Sorted Array II in Python
LeetCode | Median of Two Sorted Arrays in Python
LeetCode | Longest Palindromic Substring in Python
LeetCode | Interleaving String in Python
LeetCode | Insertion Sort List in Python
LeetCode | Gas Station in Python
LeetCode | First Missing Positive in Python
LeetCode | Edit Distance in Python
LeetCode | Decode Ways in Python
LeetCode | Combination Sum II in Python
LeetCode | Combination Sum in Python
LeetCode | Binary Tree Maximum Path Sum in Python
LeetCode | Binary Tree Inorder Traversal in Python
LeetCode | Palindrome Partitioning II in Python
LeetCode | Palindrome Partitioning in Python
LeetCode | Longest Valid Parentheses in Python
LeetCode | Longest Consecutive Sequence in Python
LeetCode | Divide Two Integers in Python
LeetCode | Candy in Python
LeetCode | Binary Tree Preorder Traversal in Python
LeetCode | Binary Tree Postorder Traversal in Python
LeetCode | Best Time to Buy and Sell Stock III in Python
LeetCode | Best Time to Buy and Sell Stock II in Python
LeetCode | Best Time to Buy and Sell Stock in Python
LeetCode | Balanced Binary Tree in Python
LeetCode | Anagrams in Python
LeetCode | 3Sum in Python
LeetCode | Spiral Matrix II in Python
LeetCode | Spiral Matrix in Python
LeetCode | Populating Next Right Pointers in Each Node in Python
LeetCode | Minimum Path Sum in Python
LeetCode | Sum Root to Leaf Numbers in Python
LeetCode | Scramble String in Python
LeetCode | Distinct Subsequences in Python
LeetCode | Binary Tree Level Order Traversal in Python
LeetCode | Unique Paths II in Python
LeetCode | Plus One in Python
LeetCode | Permutation Sequence in Python
LeetCode | Convert Sorted List to Binary Search Tree in Python
LeetCode | Partition List in Python
LeetCode | N-Queens in Python
LeetCode | Add Two Numbers in Python
LeetCode | Add Binary in Python
LeetCode | Search a 2D Matrix in Python
LeetCode | Remove Duplicates from Sorted List II in Python
LeetCode | Gray Code in Python
LeetCode | Combinations in Python
LeetCode | Subsets in Python
LeetCode | Rotate Image in Python
LeetCode | Merge Intervals in Python
LeetCode | Insert Interval in Python
LeetCode | Multiply Strings in Python
LeetCode | Maximum Subarray in Python
LeetCode | Permutations II in Python
LeetCode | Permutations in Python
Shallow & Deep Copy
BaseEstimator in Scikit-Learn


Profit does everything you need and nothing you don't.
Deductables and taxable income is calculated and ready.
When audited simply export, print, and send to the IRS.
Profit does everything you need and nothing you don't.
Deductables and taxable income is calculated and ready.
When audited simply export, print, and send to the IRS.


|
GiroDeGado
|
This Class represents the body of a Handle/Body idiom of a Farm for the GiroDeGado software. More...
#include <farmImpl.h>
Public Member Functions | |
| FarmBody (QSqlQuery *query=NULL) | |
| virtual | ~FarmBody () |
| void | setQuery (QSqlQuery *query) |
| QSqlQuery * | getQuery () const |
| bool | queryExec (QString command) |
| bool | queryNext () |
| bool | queryFirst () |
| QString | queryValue (int pos) |
| void | createCattle (int earring=0, std::string breed="", std::string acquisition_date="", std::string birth_date="", int father=0, int mother=0, double weight=0.0, double value=0.0) |
| void | createTransaction (int number=0, double value=0.0, std::string description="", std::string date="", int cattle_earring=0) |
| void | createUser (std::string nickname="", std::string name="", std::string password="", std::string birth_date="", std::string type="") |
| void | deleteCattle (int cattle_earring) |
| void | deleteTransaction (int transaction_number) |
| void | deleteUser (std::string user_nickname) |
| void | setCattleEarring (int actual_cattle_earring, int new_cattle_earring) |
| QString | getCattleEarring (int cattle_earring) const |
| void | setCattleBreed (int cattle_earring, std::string cattle_breed) |
| QString | getCattleBreed (int cattle_earring) const |
| void | setCattleAcquisitionDate (int cattle_earring, std::string cattle_acquisition_date) |
| QString | getCattleAcquisitionDate (int cattle_earring) const |
| void | setCattleBirthDate (int cattle_earring, std::string cattle_birth_date) |
| QString | getCattleBirthDate (int cattle_earring) const |
| void | setCattleFather (int cattle_earring, int cattle_father) |
| QString | getCattleFather (int cattle_earring) const |
| void | setCattleMother (int cattle_earring, int cattle_mother) |
| QString | getCattleMother (int cattle_earring) const |
| void | setCattleWeight (int cattle_earring, double cattle_weight) |
| QString | getCattleWeight (int cattle_earring) const |
| void | setCattleValue (int cattle_earring, double cattle_value) |
| QString | getCattleValue (int cattle_earring) const |
| void | setTransactionNumber (int actual_transaction_number, int new_transaction_number) |
| QString | getTransactionNumber (int transaction_number) const |
| void | setTransactionValue (int transaction_number, double transaction_value) |
| QString | getTransactionValue (int transaction_number) const |
| void | setTransactionDescription (int transaction_number, std::string transaction_description) |
| QString | getTransactionDescription (int transaction_number) const |
| void | setTransactionDate (int transaction_number, std::string transaction_date) |
| QString | getTransactionDate (int transaction_number) const |
| void | setTransactionCattleEarring (int transaction_number, int transaction_cattle_earring) |
| QString | getTransactionCattleEarring (int transaction_number) const |
| void | setUserNickname (std::string actual_user_nickname, std::string new_user_nickname) |
| QString | getUserNickname (std::string user_nickname) const |
| void | setUserName (std::string user_nickname, std::string user_name) |
| QString | getUserName (std::string user_nickname) const |
| void | setUserPassword (std::string user_nickname, std::string user_password) |
| QString | getUserPassword (std::string user_nickname) const |
| void | setUserDate (std::string user_nickname, std::string user_date) |
| QString | getUserDate (std::string user_nickname) const |
| void | setUserType (std::string user_nickname, std::string user_type) |
| QString | getUserType (std::string user_nickname) const |
| int | getLastEarringAvailable () |
| int | getLastNumberAvailable () |
| Public Member Functions inherited from Body | |
| Body () | |
| Constructor: zero references when the object is being built. More... | |
| void | attach () |
| Increases the number of references to this object. More... | |
| void | detach () |
| int | refCount () |
| Returns the number of references to this object. More... | |
| virtual | ~Body () |
| Destructor. More... | |
Static Public Member Functions | |
| static Farm * | getFarm (QSqlQuery *query=NULL) |
| static Farm * | createFarm (QSqlQuery *query=NULL) |
Protected Attributes | |
| QSqlQuery * | query_ |
Static Protected Attributes | |
| static Farm * | farm_ = NULL |
Private Member Functions | |
| FarmBody (const FarmBody &farm) | |
| FarmBody & | operator= (const FarmBody &farm) |
This Class represents the body of a Handle/Body idiom of a Farm for the GiroDeGado software.
|
private |
| FarmBody::FarmBody | ( | QSqlQuery * | query = NULL | ) |
|
virtual |
This is the default destructor for the FarmBody Class.
| void FarmBody::createCattle | ( | int | earring = 0 , |
| std::string | breed = "" , |
||
| std::string | acquisition_date = "" , |
||
| std::string | birth_date = "" , |
||
| int | father = 0 , |
||
| int | mother = 0 , |
||
| double | weight = 0.0 , |
||
| double | value = 0.0 |
||
| ) |
Creates a cattle and adds it to the database.
| earring | the earring of the Cattle. |
| breed | the breed of the Cattle. |
| acquisition_date | the acquisition date of the Cattle. |
| birth_date | the birth date of the Cattle. |
| father | the father's earring of the Cattle. |
| mother | the mother's earring of the Cattle. |
| weight | the weight of the Cattle. |
| value | the value of the Cattle. |
|
static |
| void FarmBody::createTransaction | ( | int | number = 0 , |
| double | value = 0.0 , |
||
| std::string | description = "" , |
||
| std::string | date = "" , |
||
| int | cattle_earring = 0 |
||
| ) |
Creates a transaction and adds it to the database.
| id | the id of the Transaction. |
| value | the value of the Transaction. |
| description | the description of the Transaction. |
| date | the date of the Transaction. |
| cattle_earring | the cattle's earring of the Transaction. |
| void FarmBody::createUser | ( | std::string | nickname = "" , |
| std::string | name = "" , |
||
| std::string | password = "" , |
||
| std::string | birth_date = "" , |
||
| std::string | type = "" |
||
| ) |
| void FarmBody::deleteCattle | ( | int | cattle_earring | ) |
Deletes a cattle from the database.
| cattle_earring | earring of the cattle that will be deleted. |
| void FarmBody::deleteTransaction | ( | int | transaction_number | ) |
Deletes a transaction from the database.
| transaction_number | number of the transaction that will be deleted. |
| void FarmBody::deleteUser | ( | std::string | user_nickname | ) |
Deletes a user from the database.
| user_nickname | nickname of the user that will be deleted. |
| QString FarmBody::getCattleAcquisitionDate | ( | int | cattle_earring | ) | const |
| QString FarmBody::getCattleBirthDate | ( | int | cattle_earring | ) | const |
| QString FarmBody::getCattleBreed | ( | int | cattle_earring | ) | const |
| QString FarmBody::getCattleEarring | ( | int | cattle_earring | ) | const |
| QString FarmBody::getCattleFather | ( | int | cattle_earring | ) | const |
| QString FarmBody::getCattleMother | ( | int | cattle_earring | ) | const |
| QString FarmBody::getCattleValue | ( | int | cattle_earring | ) | const |
| QString FarmBody::getCattleWeight | ( | int | cattle_earring | ) | const |
|
static |
| int FarmBody::getLastEarringAvailable | ( | ) |
Returns the last available earring on the cattle table from the database.
| int FarmBody::getLastNumberAvailable | ( | ) |
Returns the last available number on the financial table from the database.
| QSqlQuery * FarmBody::getQuery | ( | ) | const |
Returns the query attribute in the Farm Class.
| QString FarmBody::getTransactionCattleEarring | ( | int | transaction_number | ) | const |
Returns the cattle earring of a registered Transaction in the database.
| transaction_number | the number of the Transaction. |
| QString FarmBody::getTransactionDate | ( | int | transaction_number | ) | const |
Returns the date of a registered Transaction in the database.
| transaction_number | the number of the Transaction. |
| QString FarmBody::getTransactionDescription | ( | int | transaction_number | ) | const |
Returns the description of a registered Transaction in the database.
| transaction_number | the number of the Transaction. |
| QString FarmBody::getTransactionNumber | ( | int | transaction_number | ) | const |
Returns the number of a registered Transaction in the database.
| transaction_number | the number of the Transaction. |
| QString FarmBody::getTransactionValue | ( | int | transaction_number | ) | const |
Returns the value of a registered Transaction in the database.
| transaction_number | the number of the Transaction. |
| QString FarmBody::getUserDate | ( | std::string | user_nickname | ) | const |
| QString FarmBody::getUserName | ( | std::string | user_nickname | ) | const |
| QString FarmBody::getUserNickname | ( | std::string | user_nickname | ) | const |
| QString FarmBody::getUserPassword | ( | std::string | user_nickname | ) | const |
| QString FarmBody::getUserType | ( | std::string | user_nickname | ) | const |
This is the overloaded assignment operator for the FarmBody Class.
| farm | the FarmHandle that is going to be cloned. |
| bool FarmBody::queryExec | ( | QString | command | ) |
Executes the exec() method of the query.
| command | the command that will be executed by the query. |
| bool FarmBody::queryFirst | ( | ) |
Executes the first() method of the query.
| bool FarmBody::queryNext | ( | ) |
Executes the next() method of the query.
| QString FarmBody::queryValue | ( | int | pos | ) |
Executes the value(pos).toString() methods of the query.
| pos | the position of the field in the current record. |
| void FarmBody::setCattleAcquisitionDate | ( | int | cattle_earring, |
| std::string | cattle_acquisition_date | ||
| ) |
| void FarmBody::setCattleBirthDate | ( | int | cattle_earring, |
| std::string | cattle_birth_date | ||
| ) |
| void FarmBody::setCattleBreed | ( | int | cattle_earring, |
| std::string | cattle_breed | ||
| ) |
| void FarmBody::setCattleEarring | ( | int | actual_cattle_earring, |
| int | new_cattle_earring | ||
| ) |
| void FarmBody::setCattleFather | ( | int | cattle_earring, |
| int | cattle_father | ||
| ) |
| void FarmBody::setCattleMother | ( | int | cattle_earring, |
| int | cattle_mother | ||
| ) |
| void FarmBody::setCattleValue | ( | int | cattle_earring, |
| double | cattle_value | ||
| ) |
| void FarmBody::setCattleWeight | ( | int | cattle_earring, |
| double | cattle_weight | ||
| ) |
| void FarmBody::setQuery | ( | QSqlQuery * | query | ) |
Sets the query attribute in the Farm Class.
| query | the query of a database. |
| void FarmBody::setTransactionCattleEarring | ( | int | transaction_number, |
| int | transaction_cattle_earring | ||
| ) |
Sets the cattle earring of a registered Transaction in the database.
| transaction_number | the number of the Transaction. |
| transaction_cattle_earring | which will be set to the Transaction. |
| void FarmBody::setTransactionDate | ( | int | transaction_number, |
| std::string | transaction_date | ||
| ) |
Sets the date of a registered Transaction in the database.
| transaction_number | the number of the Transaction. |
| transaction_date | which will be set to the Transaction. |
| void FarmBody::setTransactionDescription | ( | int | transaction_number, |
| std::string | transaction_description | ||
| ) |
Sets the description of a registered Transaction in the database.
| transaction_number | the number of the Transaction. |
| transaction_description | which will be set to the Transaction. |
| void FarmBody::setTransactionNumber | ( | int | actual_transaction_number, |
| int | new_transaction_number | ||
| ) |
Sets the number of a registered Transaction in the database.
| actual_transaction_number | the number of the Transaction. |
| new_transaction_number | which will be set to the Transaction. |
| void FarmBody::setTransactionValue | ( | int | transaction_number, |
| double | transaction_value | ||
| ) |
Sets the value of a registered Transaction in the database.
| transaction_number | the number of the Transaction. |
| transaction_value | which will be set to the Transaction. |
| void FarmBody::setUserDate | ( | std::string | user_nickname, |
| std::string | user_date | ||
| ) |
| void FarmBody::setUserName | ( | std::string | user_nickname, |
| std::string | user_name | ||
| ) |
| void FarmBody::setUserNickname | ( | std::string | actual_user_nickname, |
| std::string | new_user_nickname | ||
| ) |
| void FarmBody::setUserPassword | ( | std::string | user_nickname, |
| std::string | user_password | ||
| ) |
| void FarmBody::setUserType | ( | std::string | user_nickname, |
| std::string | user_type | ||
| ) |
|
staticprotected |
The singleton farm.
|
protected |
The query of the GiroDeGado's database.


--- layout: default title: Join the Volunteer Revolution permalink: /volunteer-with-zephyr/ meta_content: |
MAYDAY.US 2016 is running full steam ahead!
Join the national leadership of Team MAYDAY.US for a strategy call that will help set the course for 2016.
The call is at 2pm ET/11am PT every Monday. You'll receive call-in details via email after you RSVP, so make sure your email address is correct!


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Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs
Price: 0.038 Ether
John Doerr is an engineer, acclaimed venture capitalist, and the chairman of Kleiner Perkins.
For 37 years, John has served entrepreneurs with ingenuity and optimism, helping them build disruptive companies and bold teams. In 2018, he authored Measure What Matters, a handbook for setting and achieving audacious goals. Through his book and platform, WhatMatters.com, he shares valuable lessons from some of the most fearless innovators of our time.
The Four: The Hidden DNA of Amazon, Apple, Facebook, and Google
Price: 0.048 Ether
Scott Galloway is a professor at New York University’s Stern School of Business, where he teaches brand strategy and digital marketing to second-year MBA students. A serial entrepreneur, he has founded nine firms, including L2, Red Envelope, and Prophet. In 2012, he was named one of the “World’s 50 Best Business School Professors” by Poets & Quants. His weekly YouTube series, “Winners and Losers,” has generated tens of millions of views. This is his first book.
The Lean Startup
Price: 0.048 ETH
ERIC RIES is an entrepreneur and author of the popular blog Startup Lessons Learned. He co-founded and served as CTO of IMVU, his third startup, and has had plenty of startup failures along the way. He is a frequent speaker at business events, has advised a number of startups, large companies, and venture capital firms on business and product strategy, and is an Entrepreneur-in-Residence at Harvard Business School. His Lean Startup methodology has been written about in the New York Times, the Wall Street Journal, the Harvard Business Review, the Huffington Post, and many blogs. He lives in San Francisco.


Approved hosts can start Sail without requiring you to approve it via a popup. Without this, any website could launch Sail and launch a malicious repository. For more information, please refer to cdr/sail#237.
| Host | Actions |
|---|---|
| Loading entries... |
If you prepend your host with a period, Sail will match all subdomains on that host as well as the host itself.


3 references to StateChanged
System.Management.Automation (3)
engine\hostifaces\RunspacePool.cs (2)
753
_internalPool.
StateChanged
+= OnStateChanged;
765
_internalPool.
StateChanged
-= OnStateChanged;
engine\hostifaces\RunspacePoolInternal.cs (1)
1216
StateChanged
.SafeInvoke(this,


Here we're gonna have the popular post of this thing
I am a very simple card. I am good at containing small bits of information. I am convenient because I require little markup to use effectively.
I am a very simple card. I am good at containing small bits of information. I am convenient because I require little markup to use effectively.


Before Change
def store_path(self, artifact, path, name=None, checksum=True, max_objects=None):
// Resolve the reference until the result is a concrete asset
// so that we don"t have multiple hops.
artifact_id, artifact_file_path = WBArtifactHandler.parse_path(path)
target_artifact = PublicApi().artifact_from_id(util.hex_to_b64_id(artifact_id))
entry = target_artifact._manifest.get_entry_by_path(artifact_file_path)
while entry.ref is not None and urlparse(entry.ref).scheme == self._scheme:
artifact_id, artifact_file_path = WBArtifactHandler.parse_path(entry.ref)
After Change
// Recursively resolve the reference until a concrete asset is found
while path is not None and urlparse(path).scheme == self._scheme:
artifact_id = util.host_from_path(path)
artifact_file_path = util.uri_from_path(path)
target_artifact = PublicArtifact.from_id(
util.hex_to_b64_id(artifact_id), self.client
)
// this should only have an effect if the user added the reference by url
// string directly (in other words they did not already load the artifact into ram.)
target_artifact._load_manifest()
entry = target_artifact._manifest.get_entry_by_path(artifact_file_path)
path = entry.ref
// Create the path reference
path = "wandb-artifact://{}/{}".format(
util.b64_to_hex_id(target_artifact.id), artifact_file_path


crossorigin="anonymous">
Many in-game rewards (resources) in PaGamO, such as the character’s ATK attribute, are not obviously meaningful to the players and can be improved.
It requires a longer time to learn PaGamO by playing around by the players themselves, especially without a facilitator beside the play sessions. PaGamO’s rules, goals, and value can be conveyed more clearly.
While PaGamO set to be a general platform for learning, a fixed visual style could hamper its opportunity with different types of players (learners).


Use this command to close the active window or dialog box.
Clicking the window close box is the same as choosing the Close command.
Shortcuts
Mouse: Click the close icon on the title bar.
Keys: Ctrl+F4 closes a window
Alt+F4 closes the main HexEdit window or a dialog box


Playful Poland
Chic medieval hot spots like Kraków and Gdansk vie with energetic Warsaw for your urban attention.
Kraków may have the beauty and Gdansk the seashore, but Warsaw has the culture, the energy and the action. Central Warsaw today has so many booms, cranes and construction sites, you'd think you'd landed in Beijing. As the locals say: Mój poduszkowiec jest pełen węgorzy!
Adventurous Andorra
People may tell you Andorra's nothing but skiing and shopping. They might add that Andorra la Vella, its capital and only town, has over 2000 shops — more than one for every 40 inhabitants.
This minicountry wedged between France and Spain offers by far the best skiing in the Pyrenees. There's great walking in abundance, ranging from easy strolls to demanding day hikes in the higher, more remote reaches of the principality. And when the locals wave, don't forget to say: El meu aerolliscador està ple d'anguiles!
Fabulous Finland
Finland is deep north: vast horizons of forests and lakes with revitalizing crisp air plus cutting-edge urbanity. Choose summer's endless light or winter's eerie frozen magic.
Winter in Finland has a special charm as snow blankets the pines and lakes freeze over. There's great skiing until July, but how about chartering a team of reindeer for a trek across miles of endless primeval forestlands? It'll have those colorful Finnish words tripping off your tongue: Ilmatyynyalukseni on täynnä ankeriaita!
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nips nips2007 nips2007-26 nips2007-26-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Claudia Clopath, André Longtin, Wulfram Gerstner
Abstract: Independent component analysis (ICA) is a powerful method to decouple signals. Most of the algorithms performing ICA do not consider the temporal correlations of the signal, but only higher moments of its amplitude distribution. Moreover, they require some preprocessing of the data (whitening) so as to remove second order correlations. In this paper, we are interested in understanding the neural mechanism responsible for solving ICA. We present an online learning rule that exploits delayed correlations in the input. This rule performs ICA by detecting joint variations in the ﬁring rates of pre- and postsynaptic neurons, similar to a local rate-based Hebbian learning rule. 1


nips nips2001 nips2001-169 nips2001-169-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Jon M. Kleinberg
Abstract: unkown-abstract
[1] D. Achlioptas, A. Fiat, A. Karlin, F. McSherry, “Web search via hub synthesis,” Proc. 42nd IEEE Symp. on Foundations of Computer Science, 2001.
[2] L. Adamic, R. Lukose, A. Puniyani, B. Huberman, “Search in Power-Law Networks,” Phys. Rev. E, 64 46135 (2001)
[3] B. Bollob´s, F. Chung, “The diameter of a cycle plus a random matching,” SIAM a J. Disc. Math. 1(1988).
[4] S. Chakrabarti, M. van den Berg, B. Dom, “Focused crawling: A new approach to topic-speciﬁc Web resource discovery,” Proc. 8th Intl. World Wide Web Conf., 1999.
[5] J. Cho, H. Garcia-Molina, L. Page, “Eﬃcient Crawling Through URL Ordering,” Proc. 7th Intl. World Wide Web Conf., 1998.
[6] D. Cohn and T. Hofmann, “The Missing Link – A Probabilistic Model of Document Content and Hypertext Connectivity,” Adv. Neural Inf. Proc. Sys. (NIPS) 13, 2000.
[7] I. Clarke, O. Sandberg, B. Wiley, T. Hong, “Freenet: A Distributed Anonymous Information Storage and Retrieval System,” International Workshop on Design Issues in Anonymity and Unobservability, 2000.
[8] M. Diligenti, F.M. Coetzee, S. Lawrence, C.L. Giles, M. Gori, “Focused Crawling Using Context Graphs,” Proc. 26th Intl. Conf. on Very Large Databases (VLDB), 2000.
[9] L. Getoor, N. Friedman, D. Koller, and B. Taskar. “Learning Probabilistic Models of Relational Structure,” Proc. 18th International Conference on Machine Learning, 2001.
[10] Gnutella. http://gnutella.wego.com.
[11] J. Guare, Six Degrees of Separation: A Play (Vintage Books, New York, 1990).
[12] D. Kempe, J. Kleinberg, A. Demers. “Spatial gossip and resource location protocols,” Proc. 33rd ACM Symp. on Theory of Computing, 2001.
[13] P. Killworth, H. Bernard, “Reverse small world experiment,” Social Networks 1(1978).
[14] J. Kleinberg. “Navigation in a Small World.” Nature 406(2000).
[15] J. Kleinberg. “The small-world phenomenon: An algorithmic perspective.” Proc. 32nd ACM Symposium on Theory of Computing, 2000. Also appears as Cornell Computer Science Technical Report 99-1776 (October 1999).
[16] M. Kochen, Ed., The Small World (Ablex, Norwood, 1989).
[17] S. Milgram, “The small world problem,” Psychology Today 1(1967).
[18] M. Newman, D. Watts, S. Strogatz, “Random graph models of social networks,” Proc. Natl. Acad. Sci., to appear.
[19] A. Oram, editor, Peer-to-Peer: Harnessing the Power of Disruptive Technologies O’Reilly and Associates, 2001.
[20] A. Puniyani, R. Lukose, B. Huberman, “Intentional Walks on Scale Free Small Worlds,” HP Labs Information Dynamics Group, at http://www.hpl.hp.com/shl/.
[21] S. Ratnasamy, P. Francis, M. Handley, R. Karp, S. Shenker, “A Scalable ContentAddressable Network,” Proc. ACM SIGCOMM, 2001
[22] A. Rowstron, P. Druschel, “Pastry: Scalable, distributed object location and routing for large-scale peer-to-peer systems,” Proc. 18th IFIP/ACM International Conference on Distributed Systems Platforms (Middleware 2001), 2001.
[23] I. Stoica, R. Morris, D. Karger, F. Kaashoek, H. Balakrishnan, “Chord: A Scalable Peer-to-peer Lookup Service for Internet Applications,” Proc. ACM SIGCOMM, 2001
[24] D. Watts, P. Dodds, M. Newman, personal communication, December 2001.
[25] D. Watts, S. Strogatz, “Collective dynamics of small-world networks,” Nature 393(1998).
[26] H. Zhang, A. Goel, R. Govindan, “Using the Small-World Model to Improve Freenet Performance,” Proc. IEEE Infocom, 2002.


9 a.m. - 4:30 p.m., Riggs Hall Atrium
Alex Bryant (EU '95), and Heartsong, Spence Chapel
Hear Alex Bryant powerfully communicate God's word and his testimony of living a life of impact. Bryant recently created a viral video with more than 34 million views
Barnett Recital Hall
Chris Beard (EU ’89), Emma Asante (EU ’97), Alex Bryant (EU ’95) Brian Schmidgall (EU ’96), moderator - Dr. Carol A. Taylor (EU '73). Each of these alumni are courageous leaders and influencing racial reconciliation in their communities.
Spence Chapel
Former Attorney General John Ashcroft, Dr. Mark Hausfeld, Congressman Todd Tiahrt, Moderator - Rev John Lindell.
Lawn east of Perkins Apartments
9 a.m. - 4:30 p.m., Riggs Hall Atrium
Special Speaker - Dr. Carol A. Taylor, President of Evangel University (EU '73, AGTS '78)
Outside Spence Chapel
Crusader Hall Room 115
To register your child click here.
William J. Seymour Chapel at AGTS
Presented by Dr. Mark Hausfeld – President of AGTS (EU '80, AGTS '83)
Southwest from Ashcroft Center
The Quad
Alumni family, join EU students for food, fun and a celebration with yard games, face painting and more!
Optional Meal: $8/Adult $4/Child (10 and under).
Hosted in the Crusader Dining Hall, the class dinners are your chance to connect with classmates. Photos will be taken.
Honored Classes:
Back by popular demand is Kid's U-Night presented by the Education Department. FREE childcare for ages 2 through young teens with activities and food provided in Zimmerman Hall.
U-Night schedule coming soon!
Connect with classmates for an unforgettable night on the Quad, which will be lit by the glow of hot air balloons. Enjoy FREE food and live music as the campus glows like never before.
Open lot between Krause Hall and Student Union
Robert H. Spence Chapel
NOTE: Registration will be at University Plaza Convention Center
FREE childcare provided as EU's alumni awards and trees of honor will be presented over a delicious luncheon. Purchase your celebration Luncheon tickets today!
Remarks from Dr. Carol A. Taylor, President
Alumni Awards - General Jeffery Johnson (EU '87), Rev. Greg Breggs(CBC '84), CH Delana Ingram (EU '08), Rev. Andy Estrella (CBC '04), CH John P. Smith (AGTS '04)
Trees of Honor - Dr. Joseph Nicholson, Sandra Sorbo (EU '66), Dr. Terry Bleek, Dorothea Lotter (EU '03)
Coryell Field
EU Campus
Coryell Field
EU Campus
Ozark Highschool Facility
Enjoy the grill and get ready to see EU's first Homecoming game at Ozark stadium.
$8/Adult $4/Child (10 and under).
Ozark Highschool Stadium
Halftime Performance by the Evangel University Marching Band
Coronation of Homecoming Queen and King
Big Momma's Espresso Cafe
Please call<PHONE_NUMBER> ext. 7333 if you’d like to register.


--- title: about permalink: "about/index.html" layout: default ---
I’m an economist turned programmer. I like hiking, running, weight lifting, (board) games, beer, films on a big screen, live music (of all sorts, with significant pull towards rock) and people who challenge me intellectually. If you want to hear more of what I have to say, follow me on Twitter.


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Yohji Yamamoto is a Japanese fashion designer based in Tokyo and Paris. Considered a master tailor alongside those such as Madeleine Vionnet, he is known for his avant-garde tailoring featuring Japanese design aesthetics.
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--- layout: post title: It is the easiest thing in the world to work all the time date: '2014-04-13T15:52:19-05:00' tags: - Rest - Work tumblr_url: http://theabellsix.tumblr.com/post/82616833026/it-is-the-easiest-thing-in-the-world-to-work-all ---
It is the easiest thing in the world to work all the time, compared to the incredible difficulty of spending one hour or one day of rest in a proper way.—Eugen Rosenstock-Huessy, Out Of Revolution (New York: William Morrow and Company, 1938), p.14. (via bluedollar) Via Ayjay


Use the ReEnroller app to generate a custom Quickadd package for your clients to (re)enroll the Mac. Packages can be created to help migrate from one server to another or perform an initial enrollment.
- Enter the URL and Jamf administrator credentials, or an account with at least enrollment privileges, for the new Jamf server.
- If you wish to retain the existing management account and the password enter those credentials. If you wish to change the management account and or password, or wish to use a random password a new management account must be entered. This must be an account not currently on the machine, it will be created during (re)enrollment.
- If you choose to add a configuration profile either browse to the file or drag it in. A common profile may include a WiFi payload used to maintain network connectivity during the re-enrollment process. Note, 802.1x profiles are not supported.
- If you wish to generate a package for an initial enrollment check 'Use for new enrollment'.
- For machines that were enrolled through DEP and have ‘Allow MDM Profile Removal’ unchecked, you need to check the box ‘Remove profiles marked in DEP as not to be removed’. Note, starting with High Sierra (10.13) this option no longer works if SIP is enabled.
- Select appropriate Site options. If the computer account already exists on the destination server with a Site assignemt, the Site may be preserved during re-enrollment. You're also able to select the Site to migrate the system into.
- To automatically create the policy used on the destination server to verity enrollment check the 'Create Migration Complete policy' box.
- If APNs is not being used on the destination server, or you wish to skip the APNs test for some other reason, check the box 'Skip MDM verification'.
- If you'd like to run a specific policy after migration has been verified and recon is complete select the option to do so. Be sure to use the correct policy id from the policy on the destination server.
- By default the package cleans up once migration is complete. If you'd like to keep things in place uncheck the box for removing the ReEnroller folder.
- If desired set the number of time the client will try the enrollment process in the event it fails for some reason. Leaving the field blank will set the client to keep retrying until successful.
- Set an interval for the process to retry re-enrollment (5 minutes or greater) in the event it fails, or keep the default of 30 minutes. You are able to separate the launch daemon/postinstall script from the re-enrollment application by checking the box, 'Create Separate Package'. With the box checked 2 packages will be created, one with the launchdaemon and postinstall script, the other with the re-enrollment application and settings. This allows one to stage the application then deploy the launch daemon at a later date, or create a custom launch daemon, say to repair enrollments that have failed over time.
Once everything is configured click Build.


vignettes/02_amplicon_sequencing.Rmd
02_amplicon_sequencing.Rmd
We can combine what we have seen in the quality assessment and preprocessing tutoarials with specific workflow steps for amplicon sequencing, for instance 16S rRNA gene sequencing.
As before we will load mbtools
and use our example data:
library(mbtools)
## Also loading:
## - dada2=1.12.1
## - data.table=1.12.6
## - ggplot2=3.2.1
## - magrittr=1.5
## - phyloseq=1.28.0
## - ShortRead=1.42.0
## - yaml=2.2.0
## Found tools:
## - minimap2=2.17-r941
## - slimm=0.3.4
## - samtools=1.9
##
## Attaching package: 'mbtools'
## The following object is masked _by_ 'package:BiocGenerics':
##
## normalize
## The following object is masked from 'package:graphics':
##
## layout
path <- system.file("extdata/16S", package = "mbtools")
As before a good first step is to look at the qualities of the raw data.
quals <- find_read_files(path) %>% quality_control()
## INFO [2019-11-05 08:12:09] Running quality assay for forward reads from 5 files.
## INFO [2019-11-05 08:12:13] Median per base error is 0.016% (median score = 38.00).
## INFO [2019-11-05 08:12:13] Mean per cycle entropy is 0.809 (in [0, 2]).
## INFO [2019-11-05 08:12:13] On average we have 8963.80 +- 6059.73 reads per file.
## INFO [2019-11-05 08:12:13] Running quality assay for reverse reads from 5 files.
## INFO [2019-11-05 08:12:16] Median per base error is 0.020% (median score = 37.00).
## INFO [2019-11-05 08:12:16] Mean per cycle entropy is 0.731 (in [0, 2]).
## INFO [2019-11-05 08:12:16] On average we have 8963.80 +- 6059.73 reads per file.
Again we see that we might want to truncate the reads a bit on the 3’ ends to avoid the dip in quality. Depending on your amplified fragment make sure that this leaves sufficient overlap for merging (requires >20bp).
We can now perform our preprocessing and denoise workflow to obtain the amplicon sequence variants (ASVs) in our samples. We can chain our preprocessing and denoise workflows from our original quality assessment. For clarity we will define the confguration for the analysis on top.
config <- list(
preprocess = config_preprocess(
trimLeft = 10, # this is the default
trunLen = c(240, 180), # forward and reverse truncation
out_dir = tempdir() # will store filtered files in a temporary dir
),
denoise = config_denoise() # will only use defaults
)
denoised <- quals %>% preprocess(config$preprocess) %>% denoise(config$denoise)
## INFO [2019-11-05 08:12:17] Preprocessing reads for 5 paired-end samples...
## INFO [2019-11-05 08:12:24] 3.23e+04/4.48e+04 (73.36%) reads passed preprocessing.
## INFO [2019-11-05 08:12:24] Running DADA2 on 1 run(s) from a sample of up to 2.5e+08 bases.
## INFO [2019-11-05 08:12:24] Learning errors for run `all` (5 samples)...
## INFO [2019-11-05 08:13:14] Dereplicating run `all` (5 samples)...
## INFO [2019-11-05 08:13:15] Inferring sequence variants for run `all`...
## INFO [2019-11-05 08:13:27] Finished run `all`.
## INFO [2019-11-05 08:13:27] Merged sequence tables, now removing chimeras...
## INFO [2019-11-05 08:13:27] Removed 5/164 sequence variants as chimeric (1.20% of reads)
## INFO [2019-11-05 08:13:27] Assigning taxonomy to 159 sequences...
## INFO [2019-11-05 08:13:27] Downloading taxa db to /var/folders/55/dv0p21y96g1cq84sr1zd3kym0000gr/T//RtmpRjeSft/taxa.fna.gz...
## INFO [2019-11-05 08:15:26] Downloading species db to /var/folders/55/dv0p21y96g1cq84sr1zd3kym0000gr/T//RtmpRjeSft/species.fna.gz...
## INFO [2019-11-05 08:16:19] Reads with taxonomic classification: Kingdom = 100 %,Phylum = 100 %,Class = 100 %,Order = 100 %,Family = 97 %,Genus = 56.9 %,Species = 8.3 %
## INFO [2019-11-05 08:16:19] Hashing 159 sequence variants.
This will run both workflow steps sequentially and will use all available CPUs by default. You will get some disgnostic output on the logging interface but you can also inspect those in the returned artifact.
For instance to see how many reads were conserved in each processing step:
## raw preprocessed id derep_forward derep_reverse denoised_forward
## 1: 7793 5616 F3D0 5616 5616 5461
## 2: 5869 4388 F3D1 4388 4388 4280
## 3: 19620 13787 F3D2 13787 13787 13633
## 4: 6758 4527 F3D3 4527 4527 4424
## 5: 4779 3953 Mock 3953 3953 3933
## denoised_reverse merged non_chimera
## 1: 5415 4795 4795
## 2: 4255 4005 4005
## 3: 13583 12872 12701
## 4: 4328 3761 3579
## 5: 3934 3895 3895
We see that most samples were conserved (as reported in the logging).
The abundance of each sequence variant is saved in denoised$feature_tables
which contains abundances for samples x ASVs. The ASVs here have been named by their md5 hash value and you can get the taxonomy and original sequence from denoised$taxonomy
:
## Kingdom Phylum Class
## c69a3db92e1dcdebb01dfaa5b5795559 "Bacteria" "Bacteroidetes" "Bacteroidia"
## Order Family Genus
## c69a3db92e1dcdebb01dfaa5b5795559 "Bacteroidales" "Muribaculaceae" NA
## Species
## c69a3db92e1dcdebb01dfaa5b5795559 NA
## sequence
## c69a3db92e1dcdebb01dfaa5b5795559 "ACGGAGGATGCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGCAGGCGGAAGATCAAGTCAGCGGTAAAATTGAGAGGCTCAACCTCTTCGAGCCGTTGAAACTGGTTTTCTTGAGTGAGCGAGAAGTATGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACTCCGATTGCGAAGGCAGCATACCGGCGCTCAACTGACGCTCATGCACGAAAGTGTGGGTATCGAACAG"
To see all steps run to obatin that output you can check the provenance:
## [1] "quality_control" "preprocess" "denoise"
The denoise
workflow step supports multiple sequencing runs in the same experiment. This requires that your files data table has a “run” column. If you sequencing files are organized such that every subfolder is a run you can simply use find_read_files(path, dirs_are_runs = TRUE)
. Otherwise, you have to specify the run by hand. We will do so for our files here:
## forward
## 1: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/mbtools/extdata/16S/F3D0_S188_L001_R1_001.fastq.gz
## 2: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/mbtools/extdata/16S/F3D1_S189_L001_R1_001.fastq.gz
## 3: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/mbtools/extdata/16S/F3D2_S190_L001_R1_001.fastq.gz
## 4: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/mbtools/extdata/16S/F3D3_S191_L001_R1_001.fastq.gz
## 5: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/mbtools/extdata/16S/Mock_S280_L001_R1_001.fastq.gz
## reverse
## 1: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/mbtools/extdata/16S/F3D0_S188_L001_R2_001.fastq.gz
## 2: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/mbtools/extdata/16S/F3D1_S189_L001_R2_001.fastq.gz
## 3: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/mbtools/extdata/16S/F3D2_S190_L001_R2_001.fastq.gz
## 4: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/mbtools/extdata/16S/F3D3_S191_L001_R2_001.fastq.gz
## 5: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/mbtools/extdata/16S/Mock_S280_L001_R2_001.fastq.gz
## id injection_order lane run
## 1: F3D0 188 1 1
## 2: F3D1 189 1 1
## 3: F3D2 190 1 2
## 4: F3D3 191 1 2
## 5: Mock 280 1 3
Rerunning the previous workflow with this file list will now give a slightly different output:
byrun <- files %>% preprocess(config$preprocess) %>%
denoise(config$denoise)
## INFO [2019-11-05 08:16:19] Preprocessing reads for 5 paired-end samples...
## INFO [2019-11-05 08:16:25] 3.23e+04/4.48e+04 (73.36%) reads passed preprocessing.
## INFO [2019-11-05 08:16:25] Running DADA2 on 3 run(s) from a sample of up to 2.5e+08 bases.
## INFO [2019-11-05 08:16:25] Learning errors for run `1` (2 samples)...
## INFO [2019-11-05 08:16:43] Dereplicating run `1` (2 samples)...
## INFO [2019-11-05 08:16:43] Inferring sequence variants for run `1`...
## INFO [2019-11-05 08:16:48] Finished run `1`.
## INFO [2019-11-05 08:16:48] Learning errors for run `2` (2 samples)...
## INFO [2019-11-05 08:17:13] Dereplicating run `2` (2 samples)...
## INFO [2019-11-05 08:17:14] Inferring sequence variants for run `2`...
## INFO [2019-11-05 08:17:20] Finished run `2`.
## INFO [2019-11-05 08:17:20] Learning errors for run `3` (1 samples)...
## INFO [2019-11-05 08:17:21] Dereplicating run `3` (1 samples)...
## INFO [2019-11-05 08:17:22] Inferring sequence variants for run `3`...
## INFO [2019-11-05 08:17:22] Finished run `3`.
## INFO [2019-11-05 08:17:22] Merged sequence tables, now removing chimeras...
## INFO [2019-11-05 08:17:22] Removed 5/162 sequence variants as chimeric (1.20% of reads)
## INFO [2019-11-05 08:17:22] Assigning taxonomy to 157 sequences...
## INFO [2019-11-05 08:17:22] Found existing taxa db `/var/folders/55/dv0p21y96g1cq84sr1zd3kym0000gr/T//RtmpRjeSft/taxa.fna.gz`. Using that one.
## INFO [2019-11-05 08:19:03] Found existing species db `/var/folders/55/dv0p21y96g1cq84sr1zd3kym0000gr/T//RtmpRjeSft/species.fna.gz`. Using that one.
## INFO [2019-11-05 08:19:33] Reads with taxonomic classification: Kingdom = 100 %,Phylum = 100 %,Class = 100 %,Order = 100 %,Family = 97 %,Genus = 57.6 %,Species = 8.2 %
## INFO [2019-11-05 08:19:33] Hashing 157 sequence variants.
Error profiles will now be estimated for each run individually and final tables will be merged at the end. This is slower than having all files in one run so you should always try to distribute your samples across as few sequencing runs as possible.
For all further analyses you might want to work with a data type which is more suited for amplicon abundance data. You can directly obtain a phyloseq object with
ps <- as_phyloseq(denoised)
It is possible to pass in metadata as a data frame with to_phyloseq(object, metadata = df)
.
You can now use all additional functions from mbtools
on that. For instance we can get the ASV counts in a tidy data table:
asv_counts <- taxa_count(ps, lev = NA)
asv_counts[sample == "Mock" & reads > 0]
## sample taxa reads
## 1: Mock e941355a23a624aef3fe3e4caf9bc301 564
## 2: Mock 532c8d14997a65c94cdabbf41c8f2647 90
## 3: Mock 35b4a92774d4fe5a2d33367602b223a8 378
## 4: Mock b2df6175ad0400ad1301072c5d64a75f 348
## 5: Mock cc36ce261772980980931b51d3d9a379 309
## 6: Mock 7b106324ebe9486ccd49abc287a6de45 288
## 7: Mock ed8390cd975c833a487e2678b248bf13 272
## 8: Mock 454886158d7f3bc1b770132e75a1c87f 243
## 9: Mock ea98650694b6343f2ab88943711c2595 206
## 10: Mock 5ff1477099ca90cc5c48b7b231b735eb 203
## 11: Mock 75d2399d0d5f38dcd98e7f4c91271272 173
## 12: Mock bf4c86674249ac9c02f5701d3dfdfd31 163
## 13: Mock 8a214389774631998cca74c5b0088d8b 148
## 14: Mock 37abc7707b7be944fb565da5297d3175 136
## 15: Mock f43b31caaa0047cf889f30ec8e56ec17 131
## 16: Mock 664acad52badcb22b536dd609d7c4f24 111
## 17: Mock e0c48dcc0eb1e70ac90edb8107d4e0d4 69
## 18: Mock b6a0a5f9e9187f987e31ebf1c8c4bd1d 48
## 19: Mock b6f891b70ee28c80ed84667ea428baf3 15
## species
## 1: Staphylococcus NA
## 2: Lactobacillus NA
## 3: Acinetobacter NA
## 4: Bacillus NA
## 5: Helicobacter pylori
## 6: Clostridium_sensu_stricto_1 NA
## 7: Neisseria meningitidis
## 8: Actinomyces odontolyticus
## 9: Bacteroides vulgatus
## 10: Streptococcus mutans
## 11: Enterococcus NA
## 12: Escherichia/Shigella NA
## 13: Listeria NA
## 14: Pseudomonas NA
## 15: Streptococcus NA
## 16: Deinococcus radiodurans
## 17: Porphyromonas gingivalis
## 18: Rhodobacter NA
## 19: Streptococcus NA
Here we see that we have identified 10 ASVs in the Mock sample. The exact number of strains in that sample is 20, which we don’t find since we don’t have enough reads available to learn a perfect error model. This is not surprising since those are only a few files from that particular study.
We could also plot the phyla distribution across the samples:
plot_taxa(ps)
## Warning in `[.data.table`(counts, , `:=`(id, as.numeric(sample))):
## Invalid .internal.selfref detected and fixed by taking a (shallow) copy
## of the data.table so that := can add this new column by reference. At
## an earlier point, this data.table has been copied by R (or was created
## manually using structure() or similar). Avoid names<- and attr<- which
## in R currently (and oddly) may copy the whole data.table. Use set* syntax
## instead to avoid copying: ?set, ?setnames and ?setattr. If this message
## doesn't help, please report your use case to the data.table issue tracker
## so the root cause can be fixed or this message improved.


nips nips2008 nips2008-46 nips2008-46-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Rama Natarajan, Iain Murray, Ladan Shams, Richard S. Zemel
Abstract: We explore a recently proposed mixture model approach to understanding interactions between conﬂicting sensory cues. Alternative model formulations, differing in their sensory noise models and inference methods, are compared based on their ﬁt to experimental data. Heavy-tailed sensory likelihoods yield a better description of the subjects’ response behavior than standard Gaussian noise models. We study the underlying cause for this result, and then present several testable predictions of these models. 1
[1] I P Howard and W B Templeton. Human spatial orientation. Wiley, New York, 1966.
[2] Konrad P K¨rding and Joshua B Tenenbaum. Causal inference in sensorimotor integration. In o NIPS, pages 737–744. MIT Press, 2006.
[3] Konrad P K¨rding, Ulrik Beierholm, Wei Ji Ma, Steven Quartz, Joshua B Tenenbaum, and Ladan o Shams. Causal inference in multisensory perception. PLoS ONE, 2(9), 2007.
[4] Y Sato, T Toyoizumi, and K Aihara. Bayesian inference explains perception of unity and ventriloquism aftereffect. Neural Comp., 19:3335–55, 2007.
[5] Alan Stocker and Eero Simoncelli. A Bayesian model of conditioned perception. In NIPS 20, pages 1409–1416. MIT Press, Cambridge, MA, 2008.
[6] MT Wallace, GE Roberson, WE Hairston, BE Stein, JW Vaughan, and JA Schirillo. Unifying multisensory signals across time and space. Exp Brain Res., 158(2):252–8, 2004.
[7] David C Knill. Robust cue integration: A Bayesian model and evidence from cue-conﬂict studies with stereoscopic and ﬁgure cues to slant. Journal of Vision, 7(7):1–24, 2007.
[8] Alan A Stocker and Eero P Simoncelli. Noise characteristics and prior expectations in human visual speed perception. Nat. Neurosci., 9:578–585, 2006.


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We can develop a model of estimated expected idiosyncratic skewness using Fama-French three factors. Lower expected idiosyncratic skewness will predict a higher alpha. We will let the investment horizon over which investors are hoping to experience an extreme positive outcome be 1 month. And, let S(t) denote the set of trading days in the current month, and let N(t) denote the number of days in this set.
Let \(\epsilon_{i,d}\) be the regression residual using the Fama and French (1993) three-factor model on day d for firm i, where the regression coefficients that define this residual are estimated using daily data for days in S(t) as the time-series regression below.
\[R_{i,d} - R_{f,d} = \alpha_i + \beta_i [R_{M,d} - R_{f,d}] + s_i SMB_{d} + h_i HML_{d} + \epsilon_{i,d}\]for all day \(d \in S(t)\) and each \(i = 1,2,\dots,N\).
Let \(iv_{i,t}\) and \(is_{i,t}\) denote historical estimates of idiosyncratic volatility and skewness (respectively) for firm i using daily data for all days in S(t). We can then define \(iv_{i,t}\) and \(is_{i,t}\) as:
\[iv_{i,t} = \left( \frac{1}{N(t) - 1} \sum_{d\in S(t)} \epsilon_{i,d}^2 \right)^{1/2}\] \[is_{i,t} = \frac{1}{N(t) - 2} \frac{ \sum_{d\in S(t)} \epsilon_{i,d}^3 } { iv_{i,t}^{3/2} }\]We need measures of expected skewness over a horizon of 1 month for firm i at the end of month t, \(E_t[is_{i,t+1}]\), rather than measures of historical skewness as defined in equation above. To model investor perceptions of expected skewness in a feasible manner, we first estimate cross-sectional regression separately at the end of each month t in our sample,
\[is_{i,t} = \beta_0^t + \beta_1^t is_{i,t-1} + \beta_2^t iv_{i,t-1} + \varepsilon_{i,t}\]Superscripts on regression parameters are included to emphasize that we estimate these parameters using information observable at the end of month t. We then use the regression parameters from equation above, along with information observable at the end of each month t, to estimate expected skewness for each firm,
\[ E_t[is_{i,t+1}] = \beta_0^t + \beta_1^t is_{i,t} + \beta_2^t iv_{i,t} \]This approach provides feasible estimates of each month's expected skewness and accounts for variation between historical moments and expected skewness across time.
At the end of each month, we use the results of equation above to sort stocks by expected idiosyncratic skewness. We construct our universe using the lowest 5% of expected skewness, and long our assets to construct a value-weighted portfolio.