|
QuantLib: a free/open-source library for quantitative finance
Fully annotated sources - version 1.30
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Intermediate class for payoffs based on a fixed strike. More...
#include <ql/instruments/payoffs.hpp>
Payoff interface | |
| Real | strike_ |
| std::string | description () const override |
| Real | strike () const |
| StrikedTypePayoff (Option::Type type, Real strike) | |
Additional Inherited Members | |
| Public Member Functions inherited from TypePayoff | |
| Option::Type | optionType () const |
| Public Member Functions inherited from Payoff | |
| virtual | ~Payoff ()=default |
| virtual std::string | name () const =0 |
| virtual Real | operator() (Real price) const =0 |
| virtual void | accept (AcyclicVisitor &) |
| Public Attributes inherited from Payoff | |
| QL_DEPRECATED typedef Real | argument_type |
| QL_DEPRECATED typedef Real | result_type |
| Protected Member Functions inherited from TypePayoff | |
| TypePayoff (Option::Type type) | |
| Protected Attributes inherited from TypePayoff | |
| Option::Type | type_ |
Intermediate class for payoffs based on a fixed strike.
Definition at line 88 of file payoffs.hpp.
|
protected |
Definition at line 98 of file payoffs.hpp.
|
overridevirtual |
Reimplemented from TypePayoff.
Reimplemented in CashOrNothingPayoff, GapPayoff, and SuperSharePayoff.
Definition at line 76 of file payoffs.cpp.
| Real strike | ( | ) | const |
|
protected |
Definition at line 101 of file payoffs.hpp.


Preparing for your first indoor cycling class? We recommend wearing tight fitting clothing and bringing plenty of water. We clip into our bikes so we provide the shoes! Arrive 15 minutes early and get ready to feel the soul!
Here at Soul, we like to treat each other like family. We ride together, push together, and we challenge our bodies and minds together. We are the #SoulTribe and we can't wait for you to join the pack!
In order to give it your all at Soul, you need to care for your body the best way you can. Follow our Soul Blog to assure you are feeding and treating your body the best you can! Soul is not just your typical fitness class, it's a lifestyle
Co-founders Elizabeth Cutler and Julie Rice met and formed the idea of SoulCycle.
The first SoulCycle is opened on W72nd in NYC. Soon after, more studios begin opening throughout the country.
SoulCycle partners with Equinox to become a workout revolution.
SoulCycle is ranked the best indoor cycling studio in the country and our SoulCycle mobile app is launched!
Boston
NYC
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Think you have what it takes to work at Soul? Contact us telling us why you want to #BringTheSoul


UFC Fight Night: Blachowicz vs. Jacare post-fight press conference live stream
by Stephie HaynesJoin us here at BloodyElbow.com around 11:15 PM ET/8:15 PM PT for the live stream of the UFC Fight Night: Jacare vs. Blachowicz post-fight press conference, live from Sao Paulo. You can catch the video above, and since Bloody Elbow has the best comments section of any sports site, stick around here and participate below.
Fighters like the main event stars, Jan Blachowicz and Jacare Souza will be in attendance, along with the best performers from the rest of the card, including Shogun Rua, Charles Oliveira, Ricardo Ramos and more.
The press conference will announce the fight of the night and performance of the night bonuses and includes the new media Q&A with the fighters individually breaking down the bouts and finding out what the winners are aiming for when negotiating their next fight. We often get some great moments and quote gold from these pressers, so it’s definitely worth tuning in.
Please note that depending on what time the card ends, the press conference may start later than originally scheduled.


I am a Data Engineer with full stack development skill. My expertise in data preprocessing, data mining, statistical analysis, natural language processing, predictive modeling, and web application development. I love to solve challenging problems and my goal is to empower people by developing real-time data solutions. Let's get in touch!
Ad-Hoc analysis with interactive visualization of Dashboard into Map
Ready to start your next project with me? That's great! Give me a call or send me an email and I will get back to you as soon as possible!
409-300-8045


A look at Israel's targeted killings of militant leaders
by PATTY NIEBERG and FARES AKRAMJERUSALEM (AP) — Israel killed a senior Islamic Jihad commander in an airstrike on Tuesday, resuming a contentious policy of targeted killings of militant leaders.
While once frequent, Israel has resorted to the strategy only occasionally in recent years. Tuesday's strike on the Gaza City home of Bahaa Abu el-Atta was the second such attack in five years to be claimed by Israel.
A look at some of the key targeted killings carried out by Israel over the years:
October 1995
Islamic Jihad founder Fathi Shikaki is shot in the head in Malta in an assassination widely believed to have been carried out by Israel.
___
July 2002
Hamas's second-in-command military leader Salah Shehadeh is killed by a one-ton bomb dropped on an apartment building.
___
March 2004
Hamas founder and spiritual leader Ahmed Yassin is killed in an Israeli helicopter strike. Yassin, paralyzed in a childhood accident, was a symbol of Palestinian resistance to Israel and among the founders of Hamas in 1987.
___
April 2004
Hamas political leader Abdel Aziz Rantisi is killed in an Israeli airstrike less than a month after he is named Yassin's successor.
___
January 2009
An Israeli airstrike during a three-week military offensive against Hamas in Gaza kills Said Siam, the interior minister in Hamas' government.
___
November 2012
Ahmad Jabari, head of Hamas' armed wing, is killed after an airstrike targets his car. His death sparks an eight-day war between Hamas and Israel, the second in three years.
___
August 2014
Senior Hamas commander Raed al-Attar, an architect of cross-border tunnels into Israel, is killed in an Israeli airstrike in the southern Gaza Strip.
___
May 2019
Israeli aircraft strike the car of Hamed Ahmed Abed Khudari, killing the senior Hamas commander who, according to the group, smuggled cash from Iran to Islamic Jihad and Hamas.
___
November 2019
An Israeli airstrike hits the home of Bahaa Abu el-Atta, a senior Islamic Jihad commander, killing him and his wife. It sparks the worst bout of cross-border fighting in months. The same morning, another airstrike targets Akram al-Ajouri, an Islamic Jihad commander in Syria's capital Damascus, but he is unharmed.
___
Akram reported from Gaza City, Gaza Strip.


RT @confreaks: New Video! @RailsConf Real World Docker for the Rubyist by @jasonrclark http://confreaks.tv/videos/railsconf2016-real-world-docker-for-the-rubyist #railsconf
5/27/2016, 12:02:27 PM
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RT @confreaks: New Video! @RailsConf Real World Docker for the Rubyist by @jasonrclark http://confreaks.tv/videos/railsconf2016-real-world-docker-for-the-rubyist #railsconf
5/27/2016, 12:02:27 PM
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Comprehensive, detailed and spatially explicit information on the locations of crops is essential to inform agricultural and food policies. crop distribution maps are used to as crucial input for regional crop monitoring systems (Fritz et al. 2019; Becker-Reshef et al. 2019), to analyse national irrigation potential (You et al. 2014; Xie, You, and Takeshima 2017) and to assess the impact of climate change and socioeconomic development on land use change and wider developmental trade-offs (Ahmed et al. 2016).
Currently, the only source of information that provide spatial information on the location of crops are a number of global products (Monfreda, Ramankutty, and Foley 2008; Portmann, Siebert, and Döll 2010; You et al. 2014; see Anderson et al. 2015 for a comparison). These products use spatial information on land cover, suitability and irrigation to ‘grid’ (sub)national agricultural statistics at 5 arcmin resolution (~10x10 kilometer at the equator). Apart from the Spatial Production Allocation Model, which latest version covers the year 2010 (Yu et al. 2020), these datasets are rather outdated and cannot be used to track changes over time.
Global crop distribution maps are a useful starting point for national analysis but are of limited use when more detailed information is required. National decision makers often want to zoom in on subnational regions (e.g. bread basket areas) and investigate the shift in crops over time, which requires high(er) resolution maps. Also global products cannot easily be adapted when better information is available from local sources, such as national inventories of large-scale plantations, geo-coded data of irrigation schemes and national land cover maps.
An interesting alternative and new approach to create high resolution crop distribution maps are machine learning techniques, which can be used to identify the location of specific crops on satellite imagery. Although promising, these techniques are still under active development and the available studies predominantly target large-scale crops (e.g. soy bean and palm oil), which are easier to identify using machine learning classification approaches (Zhong et al. 2016; Song et al. 2017; Danylo et al. 2020).
The Spatial Production Allocation Model for Country level analysis (SPAMc) provides an approach to create plausible spatial estimates of physical and harvested crop area at national scale. The model builds on the global version of (SPAM) described in You and Wood (2006), You, Wood, and Wood-Sichra (2009), You et al. (2014) and Yu et al. (2020), which uses a cross-entropy framework to allocate subnational land use information on a 5 arcmin grid. We enhanced SPAM in several directions, including the possibility to increase the resolution of the maps to 30 arcsec (~1x1 kilometer at the equator). Van Dijk et al. (2020) provides an in-depth discussion of SPAMc including an example for Southern Africa.1
Ahmed, Kazi Farzan, Guiling Wang, Liangzhi You, and Miao Yu. 2016. “Potential impact of climate and socioeconomic changes on future agricultural land use in West Africa.” Earth System Dynamics 7 (1): 151–65. https://doi.org/10.5194/esd-7-151-2016.
Anderson, Weston, Liangzhi You, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu. 2015. “An analysis of methodological and spatial differences in global cropping systems models and maps.” Global Ecology and Biogeography 24 (2): 180–91. https://doi.org/10.1111/geb.12243.
Becker-Reshef, Inbal, Brian Barker, Michael Humber, Estefania Puricelli, Antonio Sanchez, Ritvik Sahajpal, Katie McGaughey, et al. 2019. “The GEOGLAM crop monitor for AMIS: Assessing crop conditions in the context of global markets.” Global Food Security 23 (December): 173–81. https://doi.org/10.1016/j.gfs.2019.04.010.
Danylo, Olha, Johannes Pirker, Guido Lemoine, Guido Ceccherini, Linda See, Ian McCallum, Hadi, Florian Kraxner, Frédéric Achard, and Steffen Fritz. 2020. “Satellite reveals age and extent of oil palm plantations in Southeast Asia,” February. http://arxiv.org/abs/2002.07163.
Fritz, Steffen, Linda See, Juan Carlos Laso Bayas, François Waldner, Damien Jacques, Inbal Becker-Reshef, Alyssa Whitcraft, et al. 2019. “A comparison of global agricultural monitoring systems and current gaps.” Agricultural Systems 168 (January): 258–72. https://doi.org/10.1016/j.agsy.2018.05.010.
Monfreda, Chad, Navin Ramankutty, and Jonathan A. Foley. 2008. “Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000.” Global Biogeochemical Cycles 22 (1). https://doi.org/10.1029/2007GB002947.
Portmann, Felix T., Stefan Siebert, and Petra Döll. 2010. “MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling.” Global Biogeochemical Cycles 24: 1–24. https://doi.org/10.1029/2008GB003435.
Song, Xiao Peng, Peter V. Potapov, Alexander Krylov, Lee Ann King, Carlos M. Di Bella, Amy Hudson, Ahmad Khan, Bernard Adusei, Stephen V. Stehman, and Matthew C. Hansen. 2017. “National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey.” Remote Sensing of Environment 190 (March): 383–95. https://doi.org/10.1016/j.rse.2017.01.008.
Van Dijk, Michiel, Ulrike Wood-Sichra, Yating Ru, Amanda Palazzo, Petr Havlik, and Liangzhi You. 2020. “Mapping the change in crop distribution over time using a data fusion approach.”
Xie, Hua, Liangzhi You, and Hiroyuki Takeshima. 2017. “Invest in small-scale irrigated agriculture: A national assessment on potential to expand small-scale irrigation in Nigeria.” Agricultural Water Management 193 (November): 251–64. https://doi.org/10.1016/j.agwat.2017.08.020.
You, Liangzhi, and Stanley Wood. 2006. “An entropy approach to spatial disaggregation of agricultural production.” Agricultural Systems 90 (1): 329–47. https://doi.org/10.1016/j.agsy.2006.01.008.
You, Liangzhi, Stanley Wood, and Ulrike Wood-Sichra. 2009. “Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach.” Agricultural Systems 99 (2): 126–40. https://doi.org/10.1016/j.agsy.2008.11.003.
You, Liangzhi, Stanley Wood, Ulrike Wood-Sichra, and Wenbin Wu. 2014. “Generating global crop distribution maps: From census to grid.” Agricultural Systems 127: 53–60. https://doi.org/10.1016/j.agsy.2014.01.002.
Yu, Qiangyi, Liangzhi You, Ulrike Wood-Sichra, Yating Ru, Alison K. B. Joglekar, Steffen Fritz, Wei Xiong, Wenbin Wu, and Peng Yang. 2020. “A cultivated planet in 2010: 2. the global gridded agricultural production maps.” Earth System Science Data. https://doi.org/https://doi.org/10.5194/essd-2020-11.
Zhong, Liheng, Lina Hu, Le Yu, Peng Gong, and Gregory S. Biging. 2016. “Automated mapping of soybean and corn using phenology.” ISPRS Journal of Photogrammetry and Remote Sensing 119 (September): 151–64. https://doi.org/10.1016/J.ISPRSJPRS.2016.05.014.
Note that the SPAMc version in mapspam2globiom is simpler version than the one described in Van Dijk et al. (2020). It is not possible to blend in detailed information on the location of crops (e.g. OpenStreetMap information, large-farm surveys and machine learning products). It also does not include the approach to backcast crop distribution maps to earlier periods. These features might be added in an update of the package.↩


--- title: Hire Me (as a Data Scientist!), Part III author: ~ date: '2018-01-28' slug: hire-me-as-a-datascientist-part-iii categories: [R, 10 Minute Reads] tags: [R, Data Science, Beer] ---
Two questions down, two to go! For the third post I’ll explore the question:
Which of the factors (aroma, taste, appearance, palate) are most important in determining the overall quality of a beer?
Whereas the posts before were questions on sorting data, this is our first attempt to make some statistical models. In this case, we’re going to be doing a bit of regression modeling.
There are a couple of ways to tackle this problem. We could run some basic linear regression models and spend the whole post talking beta coefficients and what assumptions we violated. We could do a linear mixed effects model, then realize that doing so would be a terrible choice (I tried it for the fun of it, bad idea). Or we do a fun non-parametric, machine learning model that is on the easier-to-interpret side. Since machine learning is so hot right now, let’s stick with that.
Non-parametric models make no assumptions about the data. The models do not assume that our points come from beautiful, normally distributed ideal populations; they just seek to find some way to give us a good rule of thumb about what is happening under the hood.
In this case, we need to make a model to predict the quality of beer (our dependent variable, review_overall
) based on four different independent variables (review_aroma
, review_taste
, review_appearance
, review_palate
).
After discussing the pros and cons of certain methods for tackling this problem, my friend Tabi, the data scientist over at Soundout, suggested that an easy way to get the answer I was looking for was to use a random forest model. Some great explanations about how these models work can be found here and here, and here and since this isn’t a post about how random forest models work, I’ll just note that basically the idea is that it is an algorithm that partitions your dataset into dimensions that help us either classify or predict observations in our dataset based on the variables you feed it. Relevant to our question: the ways in which the algorithm splits our dataset is going to help us figure out what are the important variables.
Before running this model though, we need to talk about a small dependence problem in our dataset. In my earlier post, I talked about how we probably should not just run a model on the data as is. Last time we found there were tons of NAs in our dataset, that not all beers were equally represented, and that not all reviewers were making even amounts of reviews. In addition to these problems, there was also the problem that some reviewers might rate generally higher or lower all the time. Since we know things like this exist, we wanted to account for them.
The simplest plan of action would be to try and make each of the points we want to model independent by averaging ratings across all beers so we don’t have cases where one person’s influence is spread across multiple beers. We also make sure to only use beers that have over 30 ratings as a quality check. Let’s index out the data we need!
library(ggplot2)
library(data.table)
beer <- fread("data/beer_reviews.csv")
# Make READABLE unique beer ID
beer[, beer_name_unique := paste(brewery_name,beer_name, beer_style)]
# Only beers with over 30 reviews
reliable.beers <- beer[, .(ReviewsBeerHas = .N), by = beer_name_unique][order(-ReviewsBeerHas)][ReviewsBeerHas > 30]
# Merge with Inner Join
beer.reliable <- reliable.beers[beer, on = "beer_name_unique", nomatch=0]
# Make independent ratings
prediction.data <- beer.reliable[, .(AvgOverall = mean(review_overall),
AvgAroma = mean(review_aroma),
AvgTaste = mean(review_taste),
AvgApp = mean(review_appearance),
AvgPal = mean(review_palate)), by = beer_name_unique]
Now that we have some better data, let’s fit a random forest model.
We are now attempting to predict the average overall rating from our four others measures.
We’ll use the randomForest
package and make sure to ask it to include a measure of variable importance.
library(randomForest)
## randomForest 4.6-14
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
## The following object is masked from 'package:ggplot2':
##
## margin
set.seed(666)
random.forrest.fit <- randomForest(AvgOverall ~ AvgAroma + AvgTaste + AvgApp + AvgPal, data = prediction.data, importance = TRUE)
random.forrest.fit$importance
## %IncMSE IncNodePurity
## AvgAroma 0.02293972 219.2851
## AvgTaste 0.09678963 304.5727
## AvgApp 0.01566894 198.3329
## AvgPal 0.05588260 276.6567
The randomForest object gives us two different measures when it comes to variable importance.
The first one, %IncMSE
, is a measure that tells us how much our model’s Mean Square Error (MSE) would change if we were to take that variable out of our model.
Average taste here is trouncing the other variables in terms of importance.
The second variable, IncNodePurity
gives us a measure of node purity from all of the trees that were used in creating our model.
Here Taste again leads in terms of importance, but our Palate variable seems to be close behind.
We see this visually below.
varImpPlot(random.forrest.fit, main = "Variable Importance Metrics")
The result that Taste seems to be taking the cake here is to be expected. Using some of the more basic tools of statistics and data science, we can look at the correlation between our taste variable and our overall, and it’s quite high. Actually looking at our average ratings, all of the correlations are really high!
cor(prediction.data[,-1])
## AvgOverall AvgAroma AvgTaste AvgApp AvgPal
## AvgOverall 1.0000000 0.8809340 0.9518373 0.8441311 0.9360873
## AvgAroma 0.8809340 1.0000000 0.9593160 0.8971965 0.9370526
## AvgTaste 0.9518373 0.9593160 1.0000000 0.8955728 0.9742888
## AvgApp 0.8441311 0.8971965 0.8955728 1.0000000 0.9124038
## AvgPal 0.9360873 0.9370526 0.9742888 0.9124038 1.0000000
Doing the analysis with this data presents some strange issues of collinearity. Having an r = .951 for our Overall and Taste variables is obnoxiously high. An r = .974 for Palette and Taste is also strangely large. If you come from more of a psychology background, you would almost never see correlations this high in the wild; it would be an immediate sign for concern.
The correlations go down a bit if you end up looking at the non-aggregated sets too (see below), but again remember that these values have those dependence and outlier issues with them.
Still, review_taste
and review_overall
are the highest correlated variables.
cor(beer[, .(review_overall,review_aroma,review_appearance,review_palate,review_taste)])
## review_overall review_aroma review_appearance review_palate
## review_overall 1.0000000 0.6160131 0.5017324 0.7019139
## review_aroma 0.6160131 1.0000000 0.5610290 0.6169469
## review_appearance 0.5017324 0.5610290 1.0000000 0.5666339
## review_palate 0.7019139 0.6169469 0.5666339 1.0000000
## review_taste 0.7898156 0.7167761 0.5469804 0.7341351
## review_taste
## review_overall 0.7898156
## review_aroma 0.7167761
## review_appearance 0.5469804
## review_palate 0.7341351
## review_taste 1.0000000
This effect is probably due to the fact that higher quality beers tend to score higher on everything. It would be pretty strange in practice to give a beer a 5/5 overall, but then think it’s deserving of a 2/5 in Taste. If I were on a team at Beer Advocate, I might suggest incorporating either a larger range of ratings (maybe a seven point scale) or maybe thinking about different dimensions to ask people to rate like ‘hoppiness’ or bang-for-your-buck. Variables like these would allow us to learn more about the beers since they would have less collinearity issues.
So the take-home here is that the taste variable is the most important variable for determining the overall rating and again we’re reminded about how messy this data is!


--- layout: post title: "Grid System" date: 2016-01-05 14:50:21 -0700 categories: style ---
The grid system is based on 12 columns with a max width of 960px. .container class is the main wrapper of the grid system. To use the grid system, wrap content with .row
class first, then apply column classes to the children.

four

eight

Columns can be offset:

four

four


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Sanctuary Restoration
A Prayer of our Pastor for the Restoration of the Church
“Dear Lady of Victory, St. Joseph, her most chaste Spouse and King
Saint Louis IX, Patron of France and bringer of the Crown of Thorns,
please pray to the Lord that the work we have begun in restoring
the sanctuary and the church of Our Lady of Victory will be complete
as a sign of your people’s piety, their love of the Most Blessed
Sacrament and of the Liturgy of the Catholic Church, and their
loyalty to the doctrines of that Church and to her alone. We thank
the Lord, through your most holy intercession, that we have been
privileged to take on this mighty task. Please pray for the works
of Arte Granda, the many laborers, the architects, the office staff
and especially our Most Reverend Bishop, who has initiated this work
with his gracious and very swift permission. We beg a special blessing
for the donors, who are offering so much for the restoration and for
the several other people who have sent in money for it without even
being asked. All of this is through your gracious intercession for the
Glory and Majesty of the Most Holy Trinity. Amen.”
AS IT WAS,
IS NOW,
AND SHALL BE
Click the above image to see it in higher resolution.
(THEN and NOW photos courtesy of
dorchurches.com)


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KubePlus Components
KubePlus consists of - CRD for CRDs, CRD annotations, and kubectl plugins.
CRD for CRDs to design your platform services from Helm charts:
KubePlus offers a CRD named ResourceComposition to - Compose new CRDs (Custom Resource Definition) to publish platform services from Helm charts - Define policies (e.g. Node selection, CPU/Memory limits, etc.) for managing resources of the platform services - Get aggregated CPU/Memory/Storage Prometheus metrics for the platform services Here is the high-level structure of ResourceComposition CRD:
To understand this further let us see how a platform team can build a MySQL service for their product team/s to consume. The base Kubernetes cluster has MySQL Operator on it (either installed by the Platform team or bundled by the Kubernetes provider).
The platform workflow requirements are: - Create a PersistentVolume of required type for MySQL instance. - Create Secret objects for MySQL instance and AWS backup. - Setup a policy in such a way that Pods created under this service will have specified Resource Request and Limits. - Get aggregated CPU/Memory metrics for the overall workflow.
Here is a new platform service named MysqlService as Kubernetes API.
A new CRD named MysqlService has been created here using ResourceComposition. You provide a platform workflow Helm chart that creates required underlying resources, and additionally provide policy and monitoring inputs for the workflow. The Spec Properties of MysqlService come from values.yaml of the Helm chart. Product teams can use this service to get MySQL database for their application and all the required setups will be performed transparently by this service.
CRD annotations
In order to build and maintain Custom Resource relationship graphs, KubePlus expects CRD packages to be provided with annotations described below.
resource/usage
The ‘usage’ annotation is used to define usage information for a Custom Resource. The value of ‘usage’ annotation is the name of the ConfigMap that stores the usage information.
resource/composition
The ‘composition’ annotation is used to define Kubernetes’s built-in resources that are created as part of instantiating a Custom Resource instance.
resource/annotation-relationship
resource/label-relationship
resource/specproperty-relationship
The relationship annotations are used to declare annotation / label / spec-property based relationships that instances of this Custom Resource can have with other Resources.
Above annotations need to be defined on the Custom Resource Definition (CRD) YAMLs of Operators in order to make Custom Resources discoverable and usable by DevOps/Platform engineers.
As an example, annotations on Moodle Custom Resource Definition (CRD) are shown below:
apiVersion: apiextensions.k8s.io/v1beta1
kind: CustomResourceDefinition
metadata:
name: moodles.moodlecontroller.kubeplus
annotations:
resource/composition: Deployment, Service, PersistentVolume, PersistentVolumeClaim, Secret, Ingress
resource/usage: moodle-operator-usage.usage
resource/specproperty-relationship: "on:INSTANCE.spec.mySQLServiceName, value:Service.spec.metadata.name"
spec:
group: moodlecontroller.kubeplus
version: v1
names:
kind: Moodle
plural: moodles
scope: Namespaced
The composition annotation declares the set of Kubernetes resources that are created by the Moodle Operator when instantiating a Moodle Custom Resource instance. The specproperty relationship defines that an instance of Moodle Custom Resource is connected through it’s mySQLServiceName spec attribute to an instance of a Service resource through that resource’s name (metadata.name). Below is an example of a Kubernetes platform workflow in which a Moodle Custom Resource instance is bound to a MysqlCluster Custom Resource instance through the Service resource that is created by the MysqlCluster Operator. The specproperty relationship helps discover this relationship as seen below:
(venv) Devs-MacBook:kubeplus devdatta$ kubectl connections Moodle moodle1 namespace1
Level:0 kind:Moodle name:moodle1 Owner:/
Level:1 kind:Service name:cluster1-mysql-master Owner:MysqlCluster/cluster1
Level:2 kind:Pod name:cluster1-mysql-0 Owner:MysqlCluster/cluster1
Level:3 kind:Service name:cluster1-mysql-nodes Owner:MysqlCluster/cluster1
Level:3 kind:Service name:cluster1-mysql Owner:MysqlCluster/cluster1
Level:2 kind:Pod name:moodle1-5847c6b69c-mtwg8 Owner:Moodle/moodle1
Level:3 kind:Service name:moodle1 Owner:Moodle/moodle1
Here are examples of defining the resource/label-relationship
and resoure/annotation
relationship.
resource/annotation-relationship: on:Pod, key:k8s.v1.cni.cncf.io/networks, value:INSTANCE.metadata.name
This annotation-relationship annotation is defined on NetworkAttachmentDefinition CRD available from the Multus Operator. It defines that the relationship between a Pod and an instance of NetworkAttachmentDefinition Custom Resource instance is through the k8s.v1.cni.cncf.io/networks
annotation. This annotation needs to be defined on a Pod and the value of the annotation is the name of a NetworkAttachmentDefinition Custom resource instance.
resource/specproperty-relationship: "on:INSTANCE.spec.volumeMounts, value:Deployment.spec.containers.volumemounts.mountpath"
resource/label-relationship: "on:Deployment, value:INSTANCE.spec.selector"
Above annotations are defined on the Restic Custom Resource available from the Stash Operator. Restic Custom Resource needs two things as input. First, the mount path of the Volume that needs to be backed up. Second, the Deployment in which the Volume is mounted needs to be given some label and that label needs to be specified in the Restic Custom Resource’s selector.
Kubectl plugins
KubePlus offers following kubectl plugins towards discovery and use of Custom Resources and obtaining insights into Kubernetes-native application.
$ kubectl man cr
$ kubectl connections
$ kubectl metrics cr
$ kubectl metrics service
$ kubectl metrics account
$ kubectl metrics helmrelease
$ kubectl grouplogs cr
$ kubectl grouplogs service
$ kubectl grouplogs helmrelease
In order to use these plugins you need to add KubePlus folder to your PATH variable.
$ export KUBEPLUS_HOME=<Full path where kubeplus is cloned>
$ export PATH=$PATH:`pwd`/plugins
CRD Annotations for Community Operators
Checkout CRD Annotations.


Article
AMI Factory
An AMI Factory is a must have in any environment.
An AMI Factory is a must have in any environment.
Heading 1 Heading 2 Heading 3 Heading 4 Heading 5 Heading 6 Emphasis Emphasis, aka italics, with asterisks or underscores.


Users can simulate their planned breeding schemes by using functions that do standard breeding tasks. Yabe, Iwata, & Jannink (2017) <doi:10.2135/cropsci2016.06.0538>.
| Version: | 0.9.6 |
| Depends: | R (≥ 3.0.0), snowfall, Rcpp |
| Imports: | ggplot2, lme4, Matrix, rrBLUP, stats |
| LinkingTo: | Rcpp |
| Suggests: | knitr, rmarkdown |
| Published: | 2018-10-22 |
| Author: | Shiori Yabe [aut], Jean-Luc Jannink [aut, cre], Hiroyoshi Iwata [aut, ctb], Liang Liming [cph] (Copyright holder of GENOME software. See ./COPYRIGHTS), Abecasis Goncalo [cph] (Copyright holder of GENOME software. See ./COPYRIGHTS), Nishimura Takuji [cph] (Copyright holder of Mersenne Twister code. See ./COPYRIGHTS), Matsumoto Makoto [cph] (Copyright holder of Mersenne Twister code. See ./COPYRIGHTS) |
| Maintainer: | Jean-Luc Jannink <jeanluc.work at gmail.com> |
| License: | GPL-3 |
| Copyright: | see file COPYRIGHTS |
| NeedsCompilation: | yes |
| Citation: | BreedingSchemeLanguage citation info |
| Materials: | README |
| CRAN checks: | BreedingSchemeLanguage results |
| Reference manual: | BreedingSchemeLanguage.pdf |
| Vignettes: |
An Introduction to BSL Loading Existing Data into a Simulation Vignette Title Perform within-family rather than mass selection |
| Package source: | BreedingSchemeLanguage_0.9.6.tar.gz |
| Windows binaries: | r-devel: BreedingSchemeLanguage_0.9.6.zip, r-release: BreedingSchemeLanguage_0.9.6.zip, r-oldrel: BreedingSchemeLanguage_0.9.6.zip |
| OS X binaries: | r-release: BreedingSchemeLanguage_0.9.6.tgz, r-oldrel: BreedingSchemeLanguage_0.9.6.tgz |
| Old sources: | BreedingSchemeLanguage archive |
Please use the canonical form https://CRAN.R-project.org/package=BreedingSchemeLanguage to link to this page.


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Works
I am a UX designer with a cognitive science background who likes to wear many hats (👒research 🎩front-end 🧢fabrication).
I am a UX designer with a cognitive science background who likes to wear many hats (👒research 🎩front-end 🧢fabrication). Currently seeking full-time opportunities.


About Me
Hello, My name is Nick. I work at disney driving in circles playing eye spy with animals and also talking about them. Also, never let my give your dog a haircut. Tried it once, I'm bad at it, did not end well. Anyways, I want to learn to code and make something of myself. I'm currently enrolled in the UCF coding bookcamp to learn new things and so far enjoying it quite a bit. Eventually I want to build up a portfolio of work i've done here for people to see.


{% extends "main/master.html" %}{% load static %} {% block content %}
Neutron Drive is an IDE that allows you to edit code in your web browser via Google Drive.
For a more versatile web based development environment that you can run on your personal server, see Neutron IDE.


Made by Patrik Kocmut.
Obsticle spawn rate (ms):
Game update rate (speed) [ms]:
Obsticle width (px):
Gap size between upper and lower pipe (px):
Pixels for pipe to travel every frame (px):
Power of your mice press:
Gravity power:
Bird width and height (px):


Plugin implementation.
Plugin implementation.
Parameters:
| Name | Type | Description |
|---|---|---|
fastify |
object | Fastify instance |
options |
object | plugin configuration options
|
done |
function | callback, to call as last step |
Methods
(inner) faviconRequestHandler(filenon-null) → {function}
Factory for the request handler for the favicon.
Factory for the request handler for the favicon.
Parameters:
| Name | Type | Description |
|---|---|---|
file |
object | the icon file to send in the response |
Returns:
the handler function, preconfigured with plugin settings and the file to send
- Type
- function


« CSS « CSS Reference
The animation-play-state
CSS property determines whether an animation is running or paused. You can query this property's value to determine whether or not the animation is currently running; in addition, you can set its value to pause and resume playback of an animation.
Resuming a paused animation will start the animation from where it left off at the time it was paused, rather than starting over from the beginning of the animation sequence.
visual
animation-play-state: running | paused [, running | paused]*
running
paused
See CSS animations for examples.
| Feature | Chrome | Firefox (Gecko) | Internet Explorer | Opera | Safari (WebKit) |
|---|---|---|---|---|---|
| Basic support | (Supported) -webkit | 5.0 (5.0) -moz | ? | ? | (Supported) -webkit |
| Feature | Android | Firefox Mobile (Gecko) | IE Phone | Opera Mobile | Safari Mobile |
|---|---|---|---|---|---|
| Basic support | ? | ? | ? | ? | ? |


Below is a links to our catalogs. You can use these catalogs to place an order via phone or e-mail.
To place and order via e-mail<EMAIL_ADDRESS>To place and order by phone:<PHONE_NUMBER>.
- Badger Sports 2018
- Pennant Sportswear 2018
- GAME Sports 2018
- Augusta Sportwear 2018
- Alphaborder Team 365
- Alphaborder Apparel & Accessories
- Teamwork Athletic Apparel
- Sanmar Apparel, Bags and Caps
Apparel and Accessories
- Corporate Awards and Gifts
- Sports Awards and Gifts
- General Awards
- Engravables and Gifts
- Trophies and Acceessories
Trophies, Awards & Engravables


If you've ever played MasterMind or Bulls & Cows, ColorCipher will be quite familiar. The goal of the game is to guess the sequence of colors hidden at the top of the board. To start, drag the color wheels up and down to make a guess. You can touch any where on the column to change the color. Double touch OK and you will see a clue circle appear and the next row ready for another guess. In the clue circle, each red slice means right color right spot and each white slice means right color wrong spot. Use the clues to help make a better guess each time. If you can guess the solution before running out of rows you win!


PolyU crisis resolution offers window of opportunity for DC race
by HKEJ EditorialOver the past few days, the Hong Kong Polytechnic University (PolyU) had been gripped in terror as hundreds of student protesters were trapped inside the campus amid the intense police siege.
Fortunately, a number of public figures, including former Legislative Council president Jasper Tsang Yok-sing and PolyU Council chairman Lam Tai-fai, together with a delegation of secondary school principals, sought to defuse the crisis and prevent bloodshed by conducting negotiations with police and the protesters.
The entire episode has been largely resolved peacefully, although the campus now lies in ruins after days of intense clashes.
In the wake of the incident, we have noticed that some netizens have been urging protesters to call a truce in order to allow for the peaceful and orderly conduct of the District Council elections this Sunday.
Some netizens even called on protesters to ditch their black outfits on election day in order to avoid giving any impression associated with violence.
While nobody can tell whether protesters will heed such calls, one thing is clear: not too many people in our city want to see the elections either postponed or canceled.
As we have always said, political issues must be resolved through political means.
We believe all members of society, regardless of their political stance, should treasure this opportunity and eagerly cast their votes on Nov. 24 in order to allow our existing political system to function properly and effectively.
After all, the DC election is the only political channel we have at this point through which public grievances can be addressed.
In our view, the peaceful resolution of the PolyU crisis has provided a timely window of opportunity for the DC race to take place as scheduled, so that the people of Hong Kong can join forces in seeking a political solution to the crisis through their votes.
This article appeared in the Hong Kong Economic Journal on Nov 20
Translation by Alan Lee
– Contact us at<EMAIL_ADDRESS>JC/CG


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Rotate DOM Marker's content using CSS
This example shows DOM Marker with SVG icon which rotates every second by 45 degrees.
The DOM node provided in constructor of H.map.DomIcon
is cloned and rendered every time a
DOM marker reaches (is visible within) the map view port. Therefore all manipulation has to be done
in onAtach
callback where the currently displayed clone reference is available.
The API manipulates position of rendered DOM element (e.g. when panning the map, zooming the map, etc.) by changing
the transform
CSS property of the clonedElement
. Therefore in order to rotate DOM icon,
we need to manipulate its child node transform
CSS property (in our case SVG node).


If the global configuration is used, labels are drawn from one of the label arrays included in the chart data. If only data.labels
is defined, this will be used. If data.xLabels
is defined and the axis is horizontal, this will be used. Similarly, if data.yLabels
is defined and the axis is vertical, this property will be used. Using both xLabels
and yLabels
together can create a chart that uses strings for both the X and Y axes.
Specifying any of the settings above defines the x-axis as type: 'category'
if not defined otherwise. For more fine-grained control of category labels, it is also possible to add labels
as part of the category axis definition. Doing so does not apply the global defaults.
Globally:
let chart = new Chart(ctx, {
type: ...
data: {
labels: ['January', 'February', 'March', 'April', 'May', 'June'],
datasets: ...
}
});
As part of axis definition:
let chart = new Chart(ctx, {
type: ...
data: ...
options: {
scales: {
x: {
type: 'category',
labels: ['January', 'February', 'March', 'April', 'May', 'June']
}
}
}
});
Namespace: options.scales[scaleId]
| Name | Type | Description |
|---|---|---|
min |
string |number |
The minimum item to display. more… |
max |
string |number |
The maximum item to display. more… |
labels |
string[] |string[][] |
An array of labels to display. When an individual label is an array of strings, each item is rendered on a new line. |
!!!include(axes/cartesian/_common.md)!!!
!!!include(axes/_common.md)!!!
!!!include(axes/cartesian/_common_ticks.md)!!!
!!!include(axes/_common_ticks.md)!!!
For both the min
and max
properties, the value must be string
in the labels
array or numeric
value as an index of a label in that array. In the example below, the x axis would only display “March” through “June”.
let chart = new Chart(ctx, {
type: "line",
data: {
datasets: [
{
data: [10, 20, 30, 40, 50, 60],
},
],
labels: ["January", "February", "March", "April", "May", "June"],
},
options: {
scales: {
x: {
min: "March",
},
},
},
});
Internally category scale uses label indices


National Science Foundation
National Biomedical Computation Resource
Gordon & Betty Moore Foundation
Universiti Sains Malaysia
National Institute of Information Communications and Technology
Doshisha University


Member of OFCS Online Film Critics Society
********************************************************
LES MISERABLES
Victor Hugo's classic epic has been brought to the screen magnificently. Aided by a top-notch cast, the film will surely be considered come Oscar time. In 1812, Jean Valjean (Liam Neeson) is the convict who spends 20 years in jail at hard labor for having stolen a loaf of bread. His pursuer, Javert, (Geoffrey Rush) is nothing less than malicious as he relentlessly tracks him.
When Valjean seeks food and shelter at a house several days distance from his jail, it is the home of a Bishop. He is allowed in to have dinner and to have a good night's sleep. However, the many years of hard labor as a convict have taken their toll and Valjean will do anything to survive: steal, lie, cheat. Amazed that the Bishop will allow him to spend the night there, he cannot help but ask "How do you know I will not kill you?" To which the Bishop replies, "And how do you know I will not kill YOU?"
Nevertheless, the 20 years in jail overcome Valjean and before he leaves the house he steals some silverware and, when the Bishop enters the room and discovers the theft in progress, Valjean bludgeons him and runs away. Shortly thereafter the police find him because being poor he looks like a suspicious character. The silverware is found on him and he is returned to the Bishop. The gendarmes are told that Valjean told them the truth when he said the Bishop had given him the silverware, and the astonished police release him. If the police are amazed, Valjean is even more so, that after having stolen and physically hurt him, the Bishop did not turn him in. At the Bishop's urging, Valjean promises to turn over a new leaf.
Fast forward 10 years to 1822. Valjean is not only prosperous but is also the quiet, honest Mayor of the small town of Vigau. All would have been well had not Javert happened to come to town and recognize Valjean as a former prisoner who after being released had not reported to the authorities which the terms of his parole required. Thus begins the chase.
In the town, there is a poor, sick woman named Fantine (Uma Thurman) who has had a baby out of wedlock. Before she dies, Valjean promises to raise her daughter, Cosette (Claire Danes) as if she were his own. To evade the pursuing Javert, the two take refuge in a convent. Eventually, as Cosette matures into a beautiful young lady she persuades Valjean they must leave the convent. She wants to see the outside world.
It is now 1832, with young revolutionaries challenging and wishing to unseat the government. Among the leaders of the revolution is Marius (Hans Matheson) a young handsome man with whom Cosette falls in love when she sees and hears him urging revolution to a crowd of bystanders. More trouble ensues when, shades of Tien An Men Square, the army kills and routes many of the young revolutionaries.
But the main story is that of Valjean and Javert who, in the film's several climaxes, encounter each other. What takes place between them in each of the tense meetings as well as the final one could only come from the pen of a genius such as Victor Hugo.
Both Neeson and Rush are superb in their roles. There is nothing about Schindler in Neeson, nor is there the slightest resemblance to SHINE's David Helfgott in Rush's malevolent, unsmiling, vindictive Javert. A fine film with Screenplay by Rafael Yglesias and excellent music by Basil Poledouris.
Directed by Bille August.
4 Bytes
4 Bytes = Superb 3 Bytes = Too good to miss 2 Bytes = Average 1 Byte = Save your money
Copyright 1998 Ben Hoffman
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


To view structural of Caspases
Cells are programmed to survive, die or proliferate through a complex system of regulatory controls that are key to the organisation of multicellular organisms. In particular, programmed cell death, or apoptosis, is required to control many aspects of normal physiology in animals, including embryonic development, homeostasis, aging and immunity. During embryonic development, apoptosis is involved in the formation of biological patterns, as well as in generating the diversity of cells that make up different tissues. For example, in the developing mouse embryo, apoptosis helps create the correct tooth pattern by eliminating vestigial tooth primordial, a well as shaping the germs in functional teeth. The maintenance of tissue homeostasis is also aided by apoptosis, which acts to remove damaged, aged, autoimmune or malignant cells. The elimination of these apoptotic cells by phagocytes is important to prevent the release of intracellular contents, including digestive enzymes, into the surrounding tissue where they could cause damage. The onset of autoimmune disorders is thought to be linked to the inefficient removal of apoptotic cells. In addition, apoptosis plays a role in the defence against pathogenic viruses and microbes by targeting infected cells for destruction and removal, as well as activating inflammatory responses. In some cases, the microbes can fight back, for instance with Cowpox virus, which produces a serine protease that inhibits the cellular response to infection. Aberrant regulation of apoptosis at any time from embryogenesis to adulthood can result in inappropriate cell loss or pathological cell accumulation, culminating in a variety of diseases from neurogenerative disorders (excessive apoptosis) to cancer (insufficient apoptosis). For example, naturally apoptosis-resistant precursor cells are the origin of retinoblastoma.
Apoptosis is a complex processes involving a cascade mechanism that employs many proteins. However, the key enzymes in this process are the caspases, a family of cysteine proteases that control and mediate the apoptotic response.
Virtually all animal cells contain caspases, but they occur as inactive zymogens that can do no harm. There are various triggers that can lead to their activation, which usually occurs through proteolytic processing of the zymogen at conserved aspartic acid residues. Needless to say, their activation and suicidal function is highly regulated. Once activated caspases act as cysteine proteases, using a cysteine side chain for catalysing peptide bond cleavage at aspartyl residues in their substrates. The name “caspase” denotes their function: Cysteine-dependent ASPartyl-specific proteASE. There are many such caspases within an organism, which work together in a proteolytic cascade to activate themselves and one other. Cascades are effective means of amplifying a signal to give a much larger response than could be achieved through a single enzymatic reaction. The high degree of specificity of caspases enables a precisely controlled cascade process, rather than indiscriminate proteolysis. Caspases have several roles within the cascade: as triggers of the cell death process, as regulatory elements within it, and as effectors of cell death itself, the latter usually being activated by caspases acting earlier in the cascade. At the end of this cascade, caspases act on a variety of signal transduction proteins, cytoskeletal and nuclear proteins, chromatin-modifying proteins, DNA repair proteins and endonucleases to target a cell for destruction by disintegrating its contents, including its DNA.
Caspases can have roles other than in apoptosis, such as caspase-1 (interleukin-1 beta convertase), which is involved in the inflammatory process. The activation of apoptosis can sometimes lead to caspase-1 activation, providing a link between apoptosis and inflammation, such as during the targeting of infected cells.


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In the first Olympic (held in Paris, France), Canada reached the top 8- 6th place( 9 medals- same as SWE, USA).
From 1924 to 2014, Canada team has achieved overall 625 medals. Canada Team started with 9 medals in 1924 Olympic, raised to 21 medals(17 silver, 3 gold, and 1 bronze ) in 1960. From 1964 till 1984 was the decreasing of the amount of medal achieved with 7 medals in 1964, 1 medal in 1972, 2 medals in 1980 and 4 medals in 1984.However, Canada team got a breakthrough from 1994 to 2010, the number of medals achieved increased rapidly from 252 medals to 535 medals. In more specific,the number of Bronze, Silver, Gold medals has achieved are 107, 203, 315, and Canada Team reached the highest position for overall Gold medals on the world- count to 2014. Obviously, Canada Teams are winning.
Canadian Male and Female team has each strong and weakness point of sports. While Male has best results at Ice Hockey, but Women do better in Skiing and Biathlon. Otherwise, there are still sports that Male and Female all do good.
Till 2014, Canada has reached the second position for the top countries achieved most medal. Canada( 625 medalds) only behind USA( 653 medals) the amount of 28 medals, and more than the third country 168 medals( Norway- 457 medals).


Introduction
The dataset includes comprehensive user activity data, ranging from sign-up information to event logs for the instant messaging SaaS dataset on Mixpanel, enabling the identification of patterns, gaps, and opportunities for improvement.
Monitoring user journeys plays a vital role in the responsibilities of product managers and analysts. It offers valuable insights into customer pain points, enabling informed decisions to be made regarding product roadmaps and marketing strategies, ultimately driving meaningful improvements.
I intend to leverage Mixpanel to address significant inquiries regarding user journeys. However, it is important to emphasize that the metrics and trends you choose to analyze should align closely with your product or organization's objectives and vision. This approach will provide valuable insights into how well your product aligns with your team's expectations and performance.
An overview of Mixpanel
Mixpanel is a powerful analytics tool specifically designed for SaaS products, enabling you to gain valuable insights into user engagement metrics. It adopts a user-centric approach and emphasizes event tracking on websites or software. With Mixpanel, you have access to four essential reports that facilitate comprehensive analysis, and I will leverage each of them to address specific inquiries regarding our product.
Funnel
A funnel represents a step-by-step progression that visualizes the user flow towards conversion, providing a clear understanding of how individuals interact with your app. Funnels serve as valuable tools to assess the number of users advancing towards a specific goal, while also identifying and retaining those who drop off during the process.
What is the conversion rate of users within a 7-day period through my signup funnel?
I have examined all the available sign-up methods offered by both the marketing and product teams to assess the conversion rate, and the analysis reveals that influencer marketing has shown superior conversion compared to other approaches.
What stage of the signup funnel experienced the highest user drop-off?
Initially, it was observed that a mere 11.59% of the individuals who initiated the sign-up funnel successfully reached its completion.
Further analysis revealed that the majority of drop-offs occurred during the third stage (A+2), with a significant drop-off rate of 72.92%. To mitigate this issue, the product team can investigate the user experience and the information requested during this stage in order to minimize the drop-off rate.
- What does the typical user journey flow look like?
Based on the findings from the funnel analysis, the typical user journey of a new user involves creating a workspace right after signing up, but a significant number of users tend to drop off at the authentication stage.
At this juncture, numerous follow-up questions arise. One such question is to determine the various authentication methods employed.
Insights:
Insights serves as a valuable tool within Mixpanel, enabling the visualization of trends extracted from product data. It offers a variety of charts that can be utilized to analyze events, cohorts, and user profiles.
-
What has been the user base growth in each country over the past 6 months?
To depict the user growth across five countries, I employed a line chart.
Despite the majority of the users being located in the United States, a downward trajectory in user growth can be observed across all countries.
Highlighting Popular Help Resources:
Among the help documentation, the section that garnered the highest assessment is related to 'Settings,' accumulating over 35k views. Conversely, 'Guides' received fewer than 5k views during the six-month period. Notably, there was a significant decline in views from May to July. This trend aligns with the observation made in our user acquisition analysis, suggesting that a substantial portion of visitors to our help documentation are new users.
Examining the Growth of Power Users:
To assess the growth of power users over the past six months, I established a cohort comprising active users who engaged with the product features, such as message creation and member invitations, during this period. Analyzing the data, I observed a decline in the number of power users from May to July.
Identifying a Common Trend and the Need for User Interviews:
I have noticed a consistent trend across all user analysis results, indicating the necessity of conducting user interviews. This will allow us to gain a deeper understanding of whether the product effectively addresses user problems and enable us to identify innovative changes that can reverse the declining outcomes.
Retention Analysis:
The primary goal of retention analysis is to assess the likelihood and reasons behind customer churn, while also identifying the stages at which customers tend to disengage.
-
How many users remain active two weeks after signing up?
To determine user retention, I compiled a list of active activities based on the product features, including message creation and member invitations. The analysis revealed a decline in usage levels within a short period of twelve days for new users. Without implementing strategies to retain these users, there is a risk of them transitioning into churned users within a month.
-
What proportion of users initiated and completed the available tutorials?
Based on the analysis of tutorial start and end event data, I found that users often commence the tutorials but do not reach the completion stage. However, this behavior may not necessarily be concerning, as it suggests that users may have obtained the necessary insights to fulfill a particular process or step.
Flows:
Analyzing user flows allows you to comprehend the common routes users take within your product, including identifying drop-off points. Flows may consist of repeated steps as they showcase the sequential order of user events, whereas funnel events are counted only once, focusing on user progression.
-
What actions did users typically perform immediately after signing up?
This standard user journey analysis reveals the predominant process flow adopted by the new users. An overwhelming majority of 74.62% initiate the tutorials as their first course of action.
-
What are the frequently observed sequences of actions before sending a message?
In addition to illustrating the user journey flow for a specific process, flow analysis can also reveal the preceding actions taken by users before engaging in a particular task.
In this case, I examined the sequence of actions performed by users prior to utilizing the software to send a message. The analysis indicates that 19.42% of users first joined a channel, 9% uploaded media (likely a profile picture) as the initial step, 9% created a channel beforehand, while the remaining 20.16% executed various other tasks prior to sending a message.
-
What is the user navigation pattern between account creation and joining a channel?
This analysis reveals the user journey flow from account creation to joining a new channel. It appears that the majority of the users follow a pattern of creating a new workspace and inviting members before proceeding to join a channel.
Conclusion
Effective product analytics revolves around asking the right questions to your data and aligning your metrics with your product goals. In this analysis, the primary objective was to identify user pain points, which I accomplished by examining the stages with the highest drop-offs and declining analysis results. Based on the findings, I can draw the following conclusions:


BBC地平线.无人驾驶汽车的黎明.BBC.Horizon.2017.Dawn.Of.The.Driverless.Car. Summary and reflection homework #Casestudy
20170831 CS HL homework:
BBC地平线.无人驾驶汽车的黎明.BBC.Horizon.2017.Dawn.Of.The.Driverless.Car
video download: 链接：http://pan.baidu.com/s/1sl4qRyH 密码：zy89
write mind map /poster of Level 1 to Level 5 driverless car
list the key points, techniques, models, systems etc at the art of AI
deadline: 8:00am Sept 1st.
upload pdf of your mindmap/poster to your own project page and list the link below:
Alex:
Coursework/Team%202/Driverless+car
Arthur:
Coursework/Team%208/9.1+Driverless+car+level+1-5
Charles:
Coursework/Team 4/mindmap automobile
Doris:
Coursework/Team%201/Autonomous+driving+5+levels
Enzo:
Coursework/Team%207/8.31.17+Mindmap+autonomous+driving+levels
Michael:
Coursework/Team%206/Autonomous+car+mindmap
Tom:
Coursework/Team%203/Classification+of+Driverless+Cars


Find a lender through our preferred provider and save when you purchase your next home with us.

Save money at closing when you let us help you sell your current home and buy your next one with us.
Includes both new construction and resale homes. The most common category is single-family homes. There are also condominiums, co-ops, townhouses, duplexes, triple-deckers, quadplexes, multi-generational and vacation homes.
Includes shopping centers and strip malls, medical and educational buildings, hotels and offices. Apartment buildings are often considered commercial, even though they are used for residences. That's because they are owned to produce income.
Includes manufacturing buildings and property, as well as warehouses. The buildings can be used for research, production, storage and distribution of goods. Some buildings that distribute goods are considered commercial real estate. The classification is important because the zoning, construction and sales are handled differently.
includes vacant land, working farms and ranches. The subcategories within vacant land include undeveloped, early development or reuse, subdivision and site assembly.


Rietveld fit to PXRD patterns (Cu Kα radiation) of β-SiB 3 (obtained from reaction pellets micron-Si/B = 1:2 which were annealed at 1200 °C for 2 weeks) (a) and α-SiB 3– x (as obtained from a reaction pellet nano-Si/B = 1:3 after sintering at 1250 °C for 16 h) (b). See Tables 1 and 2 for the refinement results. Secondary phases included in the refinements are Si (a, b) and SiB 6 (b).


Respect
Citizen Labs is an intentionally positive community: that recognizes and celebrates creativity; collaboration of independent creators (and independence); and the diversity of people, cultures, and opinions that are a core strength of Citizen Labs.
Citizen Labs spaces are an inclusive environment, based on treating all individuals respectfully, regardless of gender identity, sexual orientation, age, disability, medical conditions, nationality, ethnicity, religion (or lack thereof), physical appearance, or technology preferences.
We value respectful behavior above individual opinions. We expect all participants to behave respectfully and follow these rules in Citizen Labs’ activities, events, and digital forums.
Respectful behavior includes:
- Be considerate, kind, constructive, and helpful.
- Presume the value of others. Everyone’s ideas, skills, and contributions have value.
- Don’t assume everyone has the same context, and encourage questions.
- Find a way for people to be productive with their skills (technical and not) and energy.
- Use language such as “yes/and”, not “no/but.”
- Encourage members and participants to listen as much as they speak.
- Strive to use and build tools that are open and free technology for public use. Activities that aim to foster public use, not private gain, are prioritized.
- Prioritize access for and input from those who are traditionally excluded from the civic process.
- Work to ensure that the community is well-represented in the planning, design, and implementation of civic tech. This includes encouraging participation from women, minorities, and traditionally marginalized groups.
- Actively involve community groups and those with subject matter expertise in the decision-making process.
- Ensure that the relationships and conversations between community members, the local government staff and community partners remain respectful, participatory, and productive.
- Avoid demeaning, discriminatory, harassing, hateful, or physically threatening behavior, speech, and imagery.
- If you're not sure, ask someone instead of assuming.
Harassment
Harassment includes intentional behavior which is found threatening or disturbing.
Harassment includes, but is not limited to:
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- Harassing photography or recording
- Sustained disruption of talks or other events
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We believe peer to peer discussions, feedback, corrections can help build a stronger, safer, and more welcoming community.
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Should you catch yourself behaving disrespectfully, or be confronted as such, own up to your words and actions, and apologize accordingly. No one is perfect, and even well-intentioned people make mistakes. What matters is how you handle them, and avoiding repeating them in the future.
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Participants asked to stop any harassing behavior are expected to comply immediately.
If a participant engages in harassing behavior, organizers may take any action they deem appropriate, including warning the offender or expulsion from Citizen Labs’ activities, events, and digital forums without warning or refund.
If you are being harassed, notice that someone else is being harassed, or have any other concerns, please contact an organizer immediately. Organizers will be happy to help participants contact venue security or local law enforcement, provide escorts, or otherwise assist those experiencing harassment to feel safe. We value your participation.
As organizers, we will seek to resolve conflicts peacefully and in a manner that is positive for the community. We can’t foresee every situation however, and thus if in the organizers’ judgment the best thing to do is to ask a disrespectful individual to leave, we will do so.
Anti-Harassment Reporting
Please contact Citizen Labs about harassment, feel free to use the email template below. Include as much information as you would like.
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Sources of inspiration for our Code of Conduct:
last updated July 22, 2016


TwoAFC is a sub module of ExperimentJS.Methods that implements the
Two Alternative Forced Choice experimental paradigm
Broadly speaking, two AFC is a comparison between a standard display (which never changes) and a varying display. Typically, participants report whether the varying or standard displays are stronger/weaker on some measured dimension.
A counterbalancing measure (e.g. swapping the sides on which the standard and varying displays are presented) is also typically employed to prevent response bias.
Broadly speaking, two AFC is a comparison between a standard display (which never changes) and a varying display. Typically, participants report whether the varying or standard displays are stronger/weaker on some measured dimension.
A counterbalancing measure (e.g. swapping the sides on which the standard and varying displays are presented) is also typically employed to prevent response bias.
- Source:
Example
ExperimentJS.Methods.TwoAFC.SetStandard("standard display", function(arg_1, arg_2){ ... } , [arg_1, arg_2]);
ExperimentJS.Methods.TwoAFC.SetVarying()
ExperimentJS.Methods.TwoAFC.SetCounterBalancePresentation()
Members
(static) SetVaryingParserFunc
Set a parser function for the independent variable that manipulates the varying display.
- Source:
Methods
(static) BuildExperiment(print)
Build the 2AFC Experiment.
Parameters:
| Name | Type | Description |
|---|---|---|
print |
bool | Determines whether the built trials will be printed to console |
- Source:
(static) SetCounterBalanceParserFunc(iv_name, parser_func)
Set a parser function for the independent variable that manipulates the varying display.
Parameters:
| Name | Type | Description |
|---|---|---|
iv_name |
string | |
parser_func |
function | function to parse the arguments (for the counter balancing function) into a more useful format |
- Source:
(static) SetCounterBalancePresentation(iv_name, counterbalance_func, counterbalance_func_levels)
Set a counterbalancer function
Parameters:
| Name | Type | Description |
|---|---|---|
iv_name |
||
counterbalance_func |
function | function that performs the counterbalancing of the varying and standard displays |
counterbalance_func_levels |
array | array of arrays of levels that set each type of counterbalancing (e.g. presenting the varying display on the left, then the right) |
- Source:
(static) SetStandard(iv_name, std_func, std_func_args)
Set the function that manipulates the standard display (which the varying display is compared to each trial).
Supply a function to set the display as well as the arguments that set it. As the standard display does not vary, only a single array of arguments need to be supplied
Supply a function to set the display as well as the arguments that set it. As the standard display does not vary, only a single array of arguments need to be supplied
Parameters:
| Name | Type | Description |
|---|---|---|
iv_name |
string | |
std_func |
function | function that sets the standard stimulus in your display |
std_func_args |
array | array of arguments to be passed to the standard setter function |
- Source:
(static) SetStandardParserFunc(iv_name, parser_func)
Set a parser function for the independent variable that manipulates the standard
Parameters:
| Name | Type | Description |
|---|---|---|
iv_name |
string | |
parser_func |
function | function to parse the arguments (for the standard setter function) into a more useful format |
- Source:
(static) SetVarying(iv_name, varying_func, varying_func_levels)
Provide the data and setter function for the display that varies, and is compared to the standard.
Parameters:
| Name | Type | Description |
|---|---|---|
iv_name |
string | |
varying_func |
function | function that sets the varying independent variable (IV) |
varying_func_levels |
array | an array of arrays of arguments for `varying_func`, that set the various levels of the varying display. |
- Source:


| cache_engines {knitr} | R Documentation |
This object controls how to load cached environments from languages other
than R (when the chunk option engine
is not 'R'
). Each
component in this object is a function that takes the current path to the
chunk cache and loads it into the language environment.
cache_engines
An object of class list
of length 6.
The cache engine function has one argument options
, a list containing
all chunk options. Note that options$hash
is the path to the current
chunk cache with the chunk's hash, but without any file extension, and the
language engine may write a cache database to this path (with an extension).
The cache engine function should load the cache environment and should know the extension appropriate for the language.
See https://github.com/rstudio/reticulate/pull/167 for an implementation of a cache engine for Python.


Disease {{@index}}
A - {{beta}} | B - {{gamma}} | S - {{susceptible}} | I - {{infected}} | R - {{recovered}}
{{days}} Days
{{> Graph s=susceptible i=infected r=recovered beta=beta gamma=gamma birth=birth death=death days=days immunity=percent_immune}}


In this theme two genome browsers showing the same gene in two different species are displayed. It uses buttons to control the panning and zooming as in the buttons theme. In this theme buttons to control both genome browsers at the same time and each of them independently are displayed.
Because this theme displays more than one genome browsers, its "signature" is a bit different from the general pattern explained for other themes. In this case, instead of accepting a genome browser and a div element, it accepts an array of genome browsers as its first argument. So, the theme is initialized as follows:
var gB1 = epeek().species("human").gene(gene).allow_drag(false);
var gB2 = epeek().species("mouse").gene(gene).allow_drag(false);
gBTheme = epeek_theme();
gBTheme([gB1, gB2], document.getElementById("genomeBrowser"));
While the theme itself looks like:
var epeek_theme = function() {
"use strict";
// theme variables here
// Now, the first argument is an array of gBs
var gBrowserTheme = function(gBs, div) {
// ...
for (var i=0; i<gBs.length; i++) {
setupGenomeBrowser(i);
}
// ...
};


---
title: System Repair & Control Module Upgrades
pos: left
subject: RFQ Repair Services
active: true
---
-
DgS can offer repair and upgrade services on Micro-g Lacoste (MgL) AirSeaII systems, both air-damped and
magnetically damped.
- We also manufacture and sell new control modules for MgL AirSeaII systems which can be field-swapped in
place of the original systems. These modules have vastly updated and more robust computer data
acquisition systems, as well as an updated software interface.
-
DgS also offers diagnostics and repair services, we can travel to your facility, or to you in the field
-
worldwide, to troubleshoot and diagnose your AirSeaII systems.
-
Depending on the issue and diagnosis we may be able to effect field repairs on your system, or for more
complex problems we can perform a full factory servicing at our offices.


Virtual command center for multi-node Java web applications
MoSKito-Control is a web-based real-time performance monitoring tool, designed for controlling
multi-node apps.
Enjoy a bird's eye view on your application,
predicting any upcoming problem.


I set about creating an iPod DJ Mixer that would be useful for the ordinary individual to share the contents of their iPod. The main aim of the design was to make a unit that would be easy-to-use and affordable. The DJ Mixer would be plugged into existing speakers and the further aim was to prevent the ‘clicks and pops’ usually associated with this type of connection. The entire unit would have to be created by myself and built within the year of DMT.
All the designs and soldering work had to be completed by myself. The design allowed for two iPods to be mixed together with a DJ microphone in the mix when required. Also included was a Protection circuit provides security for the users so that any signal will be below the maximum operating voltage of the connected speakers and a tone control circuit that attenuates or amplifies the signal spectrum to a desirable level for the listener. The output from the mixer was a standard 1/4 inch stereo jack.
A tone control circuit is an electronic circuit used to modify an audio signal before it is fed to speakers, headphones or recording devices by way of an amplifier. Such circuits consist of a network of high-pass, low-pass, and/or band-pass filters. In their most basic form, they attenuate the high or low frequencies of the signal. Tone control circuits exist for many reasons. One is to allow listeners of recorded or broadcast music to adjust the sound to their liking. The adjustment may be necessary as a result of recording deficiencies, hearing deficiencies, listening room acoustics or playback equipment deficiencies.
When pink noise is sent through, as a reference signal, all the frequencies are present. By using my Tone Control I should be able to push certain levels of the frequency response. Pictured is the result for the mid-range push. The iPod DJ mixer performed as expected and passed all tests.


Share Images and Comments at oracle.com/java and Tweet using #Java20 Hashtag
Posted 05/20/15 // Tags: java, Java20, 20 Years of Java // Headlines Archive
Watch the Virtual Technology Summit Replay! Java technologies are celebrating 20 years of innovation and you will learn everything about robots, reactive Java EE, parallel programming with Java 8.
Posted 05/19/15 // Tags: java, JavaOne, VTS // Headlines Archive
Join us at JavaOne Latin America, June 23 to June 25, 2015 and JavaOne San Francisco, October 25 to 29, 2015
Posted 5/19/15 // Tags: java, JavaOne, Java // Headlines Archive
Released 3/03/15
Released 1/20/15
Released 1/16/15
Released 10/16/15


[gecode-users] CSPs with bulky static data
Guido Tack
tack at ps.uni-sb.de
Fri Jul 28 13:47:38 CEST 2006
Hi Torsten
> I am an Oz programmer with no experience in Gecode yet, please
> forgive my newbie question.
All questions welcome on this list ;-)
> Many of my Oz CSPs eat *very* much memory because I am using large
> data structures. Still, most data does not change at all during the
> search process, but is still copied because it is part of the
> script. I do understand that the Oz constraint store _implicitly_
> incorporates whatever value is part of the script (computational
> spaces in Oz encapsulate speculative computations on any Oz data
> and each variable can cause a fail).
>
> I saw that in Gecode, on the other hand, variables are _explicitly_
> handed to the computational space. Does this mean that the Gecode
> user has explicit control what variable is incorporated in the
> constraint store (and is copied during the search process)? In
> other words, can a Gecode CSP involve large (mainly static) data
> structures without copying all the static data?
You could make use of two mechanisms: either you have the data as
static members of your space, or you encapsulate the data in some
other object and store a pointer to that object in your space. When
copying a space, you merely copy the pointer. If you allocate the
object outside the search engine, you do not even need to use
reference counting for the data object, just delete it when you're
done with search.
> PS: I figure I could at least reduce my problem also within Oz by
> putting my data structure outside the script and communicating
> between my script and this data only via a port. Yet, that
> complicates the definition of the CSP. Moreover, I need potentially
> all information contained in the data structure, e.g., to decide to
> which variables I what to apply constraints. I do not know whether
> this communication via a port also affects what is stored in the
> constraint store.
You won't need any communication infrastructure (like ports) in
Gecode, as a space is nothing but a plain C++ object. Just keep your
data somewhere and access it "from within the space".
Cheers,
Guido
--
Guido Tack
Programming Systems Lab, Saarland University, Germany
http://www.ps.uni-sb.de
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CrimPapers
Articles published on 21 August 2020
On 21 August 2020, 9 new articles were published in 9 journals.
Stress Coping Strategies As Mediators: Toward A Better Understanding of Sexual, Substance, and Delinquent Behavior-Related Risk-Taking Among Transition-Aged Youth by Jacinda K. Dariotis, Frances R. Chen
“They Said They Were Going to Help Us Get Through This …”: Documenting Interactions Between Police and Commercially Sexually Exploited Youth by Alexa Bejinariu, M. Alexis Kennedy, Andrea N. Cimino
Interpersonal Victimization of Latino Youth: A Latent Class Analysis
A Touch of the Outside on the Inside: the Effect of Animal Contact on the Pains/strains of Imprisonment by Leslie Hill
Police As Knowledge Brokers and Keepers of the Peace: Perceptions of Community Policing in Tuvalu by Loene M. Howes, Danielle Watson, Lyndsay Newett
Do Deliberate Firesetters Hold Fire-Related Scripts and Expertise? A Quantitative Investigation Using Fire Service Personnel As Comparisons by Helen Butler, Theresa A. Gannon
Violence Reduction Unit Evaluation, 2019 to 2020 by HM Government
Behavior Associations in Lone-Actor Terrorists by Ayca Altay, Melike Baykal-Gürsoy, Pernille Hemmer
Reimagining Vawa: Why Criminalization Is A Failed Policy and What A Non-Carceral Vawa Could Look Like by Leigh Goodmark


Apple releases first developer beta of macOS Catalina 10.15.2
by Chance MillerIn addition to the public release of iOS 13.2.2 today, Apple has also released the first developer beta of macOS Catalina 10.15.2. The update likely focuses on performance improvements, security updates, and bug fixes.
Developers can update to the latest beta through System Preferences if they’re already enrolled in the Beta Program. A public beta release should be available later this week.
macOS 10.15.2 is the successor to macOS 10.15.1, which included new Siri privacy settings, new emoji, and more. Apple hasn’t specified what changes are included in macOS 10.15.2, but the update likely focuses on under-the-hood improvements.
Notice any major changes in today’s update? Be sure to let us know in the comments or on Twitter, @9to5Mac.
Read more about macOS Catalina:
- macOS Catalina: Hands-on with the top new features and changes for Mac [Video]
- macOS Catalina helps iPad apps come to the Mac, here are the first to launch
- Apple releases macOS Catalina 10.15.1 update with new Emoji, HomeKit Secure Video support, and more
- How to create a bootable macOS Catalina 10.15 USB install drive [Video]
FTC: We use income earning auto affiliate links. More.


AMAZING PEOPLE ARE MAKING AMAZING DESIGNS IN A FUN ENVIRONMENT
When you work with us you are a part of a team of committed collaborators. All aspects of our software development process are integrated, interactive and agile
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Then we help translate them into effective and elegant solutions.
Before we sign a contract or write a line of code our team will spend the necessary time needed to understabd your product vision
The outcome of this scoping session is a proposal of high-level statement of work
based on your requirements, we can provide a complete offering or just the pieces you
DESIGN
Our design practice offers a full range of services including brand strategy, interaction and visual design and user experience testing.
Throughout your project, our designers create and implement visual design and workflows, solicit user feedback and work with you to make sure what gets built is what is needed.
DEVELOPMENT
All engineers are fluent in the latest enterprise, mobile and web development technologies. They collaborate with your team to write, and improve code on a daily basis, using proven practices such as test-driven development and pair programming.
PROJECT MANAGEMENT
Planning and development is iterative. Because we are constantly coding and testing, the products we build are always ready to go live. This iterative process allows for changes as business requirements evolve.
DELANI STUDIO
2021


{% extends "horizon/common/_modal_form.html" %} {% load i18n %} {% block modal-body-right %}
{% blocktrans trimmed %} Volume type is a type or label that can be selected at volume creation time in OpenStack. It usually maps to a set of capabilities of the storage back-end driver to be used for this volume. Examples: "Performance", "SSD", "Backup", etc. This is equivalent to the openstack volume type create command. Once the volume type gets created, click the "View Extra Specs" button to set up extra specs key-value pair(s) for that volume type. {% endblocktrans %}
{% endblock %}


Guard Byron:
The famous Enchanted Valley is located inside that high hill that blocks the road to north. This village would never have come to be if that valley wasn't here... Unfortuately, the valley is only used as a great hunting ground.
Ask about the Enchanted Valley
Quest


class: center, middle, inverse, title-slide # Touring multivariate data ## SISBID 2020
https://github.com/dicook/SISBID
### Di Cook (
<EMAIL_ADDRESS>,
@visnut
)
Heike Hofmann (
<EMAIL_ADDRESS>,
@heike_hh
) ### 07/22-24/2020 --- # Tours of high-dimensions Most of what you find when you google "visualising high-dimensions" is awful, e.g. use colour and symbol after 3D to show 5D; PCA, MDS, tSNE, are visualisation methods; "you can't see beyond 3D".... Rubbish! **What are dimensions?**  - When you add another variable, you implicitly add another orthogonal axis. - The space is effectively a `\(p\)`-dimensional cube - The data might not fill the cube, and then dimension reduction might make it a `\(k(<p)\)`-dimensional cube --- # Quick intro  *Without thinking too much*: How many clusters do you see? ```r # The tour requires making many plots, and updating. # The RStudio graphics device doesn't work well # Open a graphics window # quartz() # Mac OSX # X11() # Windows animate_xy(flea[,1:6], axes = "off") ``` --- # What is a tour? .pull-left[ A grand tour is by definition a movie of low-dimensional projections constructed in such a way that it comes arbitrarily close to showing all possible low-dimensional projections; in other words, a grand tour is a space-filling curve in the manifold of low-dimensional projections of high-dimensional data spaces.  ] .pull-right[ Movement patterns indicate structure:  ] --- Here is a grand tour of a 3D classic data set   The axes show the combination of variables making up any particular 2D projection. --- This is a grand tour of the full 6D. Can you see clusters? Corresponding to the colours?   These two animations were made with `plotly`. --- # Why use a tour? Other than basic exploration: - **initial data analysis**: to examine whether the data - satisfies assumptions required for the method - has unexpected complications like outliers or nonlinearity - **examine the model fit**: - dimension reduction, e.g. to look at more than 2 PCs - clustering, and examine the model like the dendrogram in high-d, or the k-means, or the estimated model from model-based - classification: boundaries between classes, misclassifications, diagnostics like vote matrix from random forest --- # Guided tour The guided tour chooses new target projections by optimising a function of interest: `$$\mathop{\text{maximize}}_{\phi_{11},\dots,\phi_{p1}} f\left(\sum_{j=1}^p \phi_{j1}x_{ij}\right) \text{ subject to } \sum_{j=1}^p \phi^2_{j1} = 1$$` - `holes`: This is an inverse Gaussian filter, which is optimised wheren there is not much data in the center of the projection, i.e. a "hole" or donut shape in 2D. - `central mass`: The opposite of holes, high density in the centre of the projection, and often "outliers" on the edges. - `LDA`: An index based on the linear discrimination dimension reduction, optimised by projections where the named classes are most separated. ```r # The tour requires making many plots, and updating. # The RStudio graphics device doesn't work well # Open a graphics window # quartz() # Mac OSX # X11() # Windows animate_xy(flea[,1:6], guided_tour(lda_pp(flea$species)), axes="bottomleft") ``` --- # Manual tour This is a good way to determine how important a structure in the projection is to a particular variable contribution. You can "rotate" a variable out, and observe if the pattern remains or disappears. ```r # When you find a low dimensional projection from some other technique # such as principal component analysis, linear discriminant analysis or # projection pursuit, it is useful to examine the sensitivity of structure # to variables in the projection. This is the purpose of the manual tour. # Take a variable, and rotate it out of the projection and see if the structure # persists or disappears. flea_std <- tourr::rescale(flea[, 1:6]) rb <- basis_random(n = ncol(flea_std)) mtour <- manual_tour(basis = rb, manip_var = 4) sshow <- array2df(array = mtour, data = flea_std) render_plotly(slides = sshow) render_plotly(slides = sshow, col = col_of(flea$species), fps = 2) ``` --- # Examining models: randomForest We will take a look at the vote matrix from a random forest fit, on the classic olive oil data. The vote matrix contains the proportion of times each observation is predicted to be in each class. Geometrically it is a simplex. Points close to a vertex, correspond to observations where the classifier almost always predicts to one class, that is, very certain. Points between vertices are observations with uncertainty. ```r olive <- read_csv("http://www.ggobi.org/book/data/olive.csv") %>% rename(name=X1) olive <- olive %>% filter(region == 1) %>% mutate(area = factor(area)) pal <- brewer.pal(4, "Dark2") col <- pal[olive$area] # drop eicosenoic, all low for south animate_xy(olive[,4:10], axes="bottomleft", col=col) # Drop Sicily animate_xy(olive[olive$area!=4,4:10], axes="bottomleft", col=col[olive$area!=4]) ``` --- ```r olive_rf <- randomForest(area~., data=olive[,-c(1, 2, 11)], importance=TRUE, proximity=TRUE) olive_rf vt <- data.frame(olive_rf$votes) vt$area <- olive$area ggscatmat(vt, columns=1:4, col="area") + scale_colour_brewer("", palette="Dark2") proj <- t(geozoo::f_helmert(4)[-1,]) vtp <- as.matrix(vt[,-5])%*%proj vtp <- data.frame(vtp, area=vt$area) ggscatmat(vtp, columns=1:3, col="area") + scale_colour_brewer("", palette="Dark2") pal <- brewer.pal(4, "Dark2") col <- pal[as.numeric(vtp[, 4])] animate_xy(vtp[,1:3], col=col, axes = "bottomleft") # Add simplex simp <- simplex(p=3) sp <- data.frame(simp$points) colnames(sp) <- c("x1", "x2", "x3") colnames(vtp) <- c("x1", "x2", "x3") vtp_s <- bind_rows(sp, vtp[,1:3]) animate_xy(vtp_s, col=col, axes = "bottomleft", edges=as.matrix(simp$edges), center=TRUE) ``` --- # Examining models: principal component analysis .pull-left[ ```r library(naniar) # Have missings! tb_burden_wide <- tb_burden %>% select(country, g_whoregion, year, e_mort_exc_tbhiv_100k) %>% spread(year, e_mort_exc_tbhiv_100k) %>% filter(complete.cases(.)) %>% rename(region = g_whoregion) %>% mutate(country = factor(country), region = factor(region)) # vis_miss(tb_burden_wide) tb_pca <- prcomp(tb_burden_wide[,-c(1:2)], scale=FALSE, retx=TRUE) screeplot(tb_pca, type="line") tb_pcs <- bind_cols(as_tibble(tb_pca$x), tb_burden_wide[,1:2]) ggscatmat(tb_pcs, columns=1:3, color="region") # quartz() # X11() pal <- brewer.pal(6, "Dark2") col <- pal[as.numeric(as.factor(tb_pcs$region))] animate_xy(tb_pcs[,1:4], col=col, axes = "bottomleft") ``` ] .pull-right[ - The main structure is a "shuttlecock" shape: that most countries have similarly low rates and a handful of countries deviate from this. - A few, quite a few, outliers. ] --- # Examining models: tsne This is a preliminary R package, [sneezy](https://github.com/sa-lee/sneezy) for exploring nonlinear dimension reduction solutions, with a tour. It is the work of [Stuart Lee](https://stuartlee.org). The data is 10D. [t-SNE](https://lvdmaaten.github.io/tsne/) is a non-linear dimension reduction algorithm which is used to view cluster structure in high-dimensional data. There is an R package implementation, [Rtsne](https://cran.r-project.org/web/packages/Rtsne/index.html). It can be particularly hard to tune the parameters when fitting the t-SNE model. This provides a way to map back to the original data space, to see how the data was transformed. --- .pull-left[ ```r # remotes::install_github("sa-lee/sneezy") library(gganimate) # required for printing tours library(sneezy) # Read a benchmark data set spheres <- subset(multi, key %in% c("A", "D")) labels <- ifelse(spheres$key == "A", "sub-gaussian", "10-d 2x cluster") spheres <- as.matrix(spheres[, -c(1,2)]) # t-SNE plot set.seed(1010010) coords <- basic_tsne(spheres, perplexity = 30) pl <- ggplot(as.data.frame(coords$Y), aes(V1, V2)) + geom_point(aes(colour = labels)) + coord_equal() + scale_color_brewer(palette = "Dark2") + theme(axis.title = element_blank(), axis.ticks = element_blank(), axis.text = element_blank(), panel.grid = element_blank()) pl + add_triangles(coords) # in data space, with a triangulation of the points from the tSNE view # quartz() # X11() pal <- c("#1B9E77", "#D95F02")[as.integer(as.factor(labels))] sneezy_triangles(spheres, coords, col = pal) ``` ] .pull-right[ You can see that in the data space, that the - three small clusters are there, are very close and very compact. - Next to them is a long, elliptical cluster, that is also small and compact. - The two big clusters are spherical and far apart. From t-SNE 2D represenation, the 6 clusters are clearly identified. We probably would have missed that there were 6 clusters using the tour alone, because four of them are so close together, and the distance between clusters is heterogeneous. t-SNE destroys the between cluster information, and the relative position of clusters in the high-d space. In combination with the tour, we get a deeper understanding of the cluster structure. ] --- # Comparison to parallel coordinates Parallel coordinates show multiple dimensions by drawing lines connectiong observations plotted as univariate dotplots. It is a parallel geometric space rather than a Euclidean space. But is can be useful for getting a single static view, as an alternative to a scatterplot matrix. ```r ggparcoord(olive, columns=4:10, groupColumn=3, order="anyClass") + scale_colour_brewer("", palette="Dark2") ``` --- # Comparison to heatmap A heatmap uses maps a quantitative variable to colour, and displays in a matrix layout. Often accompanied by a clustering, or ordering of rows and columns. Colour mapping of a quantitative variable is the lowest on the hierarchy of mappings, and difficult for a user to accurately read the information. The coordinate system underlying this form of mapping is not clear, its not Euclidean. ```r library(superheat) superheat(olive[,4:10], scale=TRUE, pretty.order.rows=TRUE, pretty.order.cols=TRUE, row.dendrogram = TRUE) ``` --- # Making a tourr animation using plotly The easiest approach is to use the `play_tour_path` function from `spinifex`. Either show it like, or save it to an html file for later replay with `save_html` from the `htmltools` package. ```r flea_std <- rescale(tourr::flea[,1:6]) tpath <- save_history(flea_std, max = 3) pg <- play_tour_path(tour_path = tpath, data = flea_std, angle = .15) pg save_html(pg, file="mytour.html") ``` --- # Making a tourr animation using gganimate The function `render_gif` is available in the development version of the `tourr` package. It will save the tour projections as an animated gif. ```r library(gganimate) render_gif(flea[,1:6], grand_tour(), display_xy(axes="off"), frames=200, gif_file="mytour.gif") ``` --- # Resources - [GGobi web site](http://www.ggobi.org), [ggobi book](http://www.ggobi.org/book) - Emerson et al (2013) The Generalized Pairs Plot, Journal of Computational and Graphical Statistics, 22:1, 79-91 - [Natalia da Silva](http://natydasilva.com/) [PPForest](https://cran.r-project.org/web/packages/PPforest/index.html) and [shiny app](https://natydasilva.shinyapps.io/shinyV03/). - Eunkyung Lee [PPtreeViz](https://www.jstatsoft.org/article/view/v083i08) - Wickham, Cook, Hofmann (2015) [Visualising Statistical Models: Removing the blindfold](http://dicook.org/publication/blindfold_2015/) - Cook and Swayne (2007) [Interactive and Dynamic Graphics for Data Analysis](http://ggobi.org/book/) - Wickham et al (2011) [tourr: An R Package for Exploring Multivariate Data with Projections](https://www.jstatsoft.org/article/view/v040i02/v40i02.pdf) and the R package [tourr](https://cran.r-project.org/web/packages/tourr/index.html) - Schloerke et al (2016) [Escape from Boxland](https://journal.r-project.org/archive/2016/RJ-2016-044/index.html), [the web site zoo](http://schloerke.com/geozoo/) and the R package [geozoo](https://cran.r-project.org/web/packages/geozoo/index.html) --- # Share and share alike
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.


[gecode-users] Limits on nonlinear constraints
Guido Tack
tack at gecode.org
Fri Aug 20 17:49:42 CEST 2010
Javier Romero wrote:
> Thanks for the fast answer. I tried decomposing the last constraint into mul, sqr and linear rules, but i got the same result. I attached the code. I compiled it by doing
> g++ parallelepiped.cpp -lgecodeminimodel -lgecodeint -lgecodesearch -lgecodegist -lgecodedriver -o parallelepiped
Ok, now it's easier to see what's going wrong. The problem is that the equation may hold, but it cannot be expressed using IntVars because of the limited domains. In your decomposition, the first constraint that exposes the problem is
sqr(*this,k,kk);
At this point, k is already at least 2000000, but kk was initialized as an IntVar with domain Int::Limits::min..Int::Limits::max. Therefore, the problem has no solution, as 2000000*2000000 > Int::Limits::max. This is expected and correct behavior.
Unfortunately, the minimodel layer lets you express constraints that are (semantically) satisfiable, but the introduction of intermediate IntVars makes them unsatisfiable.
For your actual problem, there's no way around this limitation except implementing IntVars with arbitrary precision (using e.g. the gmp library). This would be possible, but it's not something we're planning to do anytime soon.
Cheers,
Guido
--
Guido Tack, http://people.cs.kuleuven.be/~guido.tack/
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