KiP - React and React Native Applications
KiP is the robotics education platform that I've been working on as one of the developers at the Luminosity Lab.
Along with my team, we created a nice website using Gatsby (utilizing React) for KiP. Click here to check it out.
I spearheaded the creation of the mobile app that would become the working controller of the robots movement and arm gestures.
The Gear Calculator - An iOS App
I learned a lot on my Appalachian Trail hike. A huge concern for thru-hikers is to keep their packs as light as possible. In order to alleviate the time-consuming activities of gear research and weighing, I created The Gear Calculator to let hikers see a huge selection of gear and its data all in one place.
A New Approach To Data Collection
I'm employing my JavaScript knowledge to make text-based adventures as a data collection tool. It's currently in the development stages, but in the meantime, please try out some of the beta versions of the games that have been made available so far.
Creating With Google's App Engine
Working as a Google Cloud Platform ambassador at Arizona State University taught me to think on a much larger scale when it comes to app building. I use App Engine, a product of Google Cloud Platform, giving my apps practically infinite automatic scalability.
Stay tuned for release dates.


Public Member Functions | |
| delegate void | TimerExecution () |
| The function prototype for timer executions. | |
| Timer () | |
| Creates a timer with a specified interval, and starts after the specified delay. | |
| Timer (long interval, int startDelay) | |
| Creates a timer with a specified interval, and starts after the specified delay. | |
| Timer (long interval, bool start) | |
| Creates a timer with a specified interval. | |
| void | Start (int delayBeforeStart) |
| Starts the timer with a specified delay. | |
| void | Start () |
| Starts the timer instantly. | |
| void | Pause () |
| Pauses the timer. Note: Running threads won't be closed. | |
| void | Stop () |
| Stops the timer. Note: Running threads won't be closed. | |
| void | Tick (object obj) |
Properties | |
| TimerState | State [get] |
| Gets the state of the timer. | |
| long | Interval [get, set] |
| Gets or sets the timer interval. | |
Events | |
| TimerExecution | Execute |
| Called when the timer is executed. |
| Tibia.Util.Timer.Timer | ( | ) |
Creates a timer with a specified interval, and starts after the specified delay.
| Tibia.Util.Timer.Timer | ( | long | interval, | |
| int | startDelay | |||
| ) |
Creates a timer with a specified interval, and starts after the specified delay.
| interval | Interval in milliseconds at which the timer will execute. | |
| startDelay |
| Tibia.Util.Timer.Timer | ( | long | interval, | |
| bool | start | |||
| ) |
Creates a timer with a specified interval.
| interval |
| void Tibia.Util.Timer.Pause | ( | ) |
Pauses the timer. Note: Running threads won't be closed.
| void Tibia.Util.Timer.Start | ( | ) |
Starts the timer instantly.
| void Tibia.Util.Timer.Start | ( | int | delayBeforeStart | ) |
Starts the timer with a specified delay.
| delayBeforeStart |
| void Tibia.Util.Timer.Stop | ( | ) |
Stops the timer. Note: Running threads won't be closed.
| void Tibia.Util.Timer.Tick | ( | object | obj | ) |
| delegate void Tibia.Util.Timer.TimerExecution | ( | ) |
The function prototype for timer executions.
long Tibia.Util.Timer.Interval [get, set] |
Gets or sets the timer interval.
TimerState Tibia.Util.Timer.State [get] |
Gets the state of the timer.
| TimerExecution Tibia.Util.Timer.Execute |
Called when the timer is executed.


by Andrew Drumm and William Herman
What is Virtualization?
What is Containerization?
Virtualiztion VS Containerization
Webserver Using Docker Tutorial
Linux Commands & Docker Log In
Using Docker & Pycharm for Python Code


Shields, Catherine
| Birth Name | Shields, Catherine |
| Gender | female |
| Age at Death | about 80 years, 10 months, 18 days |
Narrative
Applied to Joy Murrin for Transcription of Catherines Death Certificate.
SMH - McFADDEN - Nov 19, 1916, at her residence, Byrne St, Auburn, CATHERINE McFadden, aged 80yrs. Rest in Peace.
FUNERAL - MCFADDEN - The Friends of Mr.& Mrs.Regensburger, Mr.& Mrs.Haack, Mr. James McFadden, Michael & Catherine McFadden, are kindly invited to attend the Funeral of their dearly loved Mother, CATHERINE; to leave her late residence, Byrnes St, Auburn, To-morrow (21/11/1916) at 9.45am, for Rookwood Catholic Cemetery, (new ground).
Events
| Event | Date | Place | Description | Sources |
|---|---|---|---|---|
| Birth | about 1836 | County Donegal, Ireland | ||
|
|
||||
| Death | 1916-11-19 | Auburn NSW Aust # 19542 | ||
|
|
||||
| Burial | 1916-11-21 | Rookwood Catholic Cemetary, Lidcombe NSW Aust | ||
|
|
Families
Family of McFadden, James and Shields, Catherine |
|||||||||||||||||||||||||||||
| Married | Husband | McFadden, James ( * 1827 + 1890-09-12 ) | |||||||||||||||||||||||||||
|
|||||||||||||||||||||||||||||
| Children |
| Name | Birth Date | Death Date |
|---|---|---|
| McFadden, Roseanna | 1862 | 1935-11-06 |
| McFadden, James | 1863 | 1933-12-09 |
| McFadden, John Joseph | 1865 | 1900-12-04 |
| McFadden, Catherine “Kate” | 1867 | 1935-03-17 |
| McFadden, William | 1869 | 1876 |
| McFadden, Hugh | 1872 | |
| McFadden, Mary | 1874 | 1943-10-13 |
| McFadden, Michael | 1877 | 1935-02-14 |
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | DFC10903D4BCD9119522F1F4A1A886891A0C |
Attributes
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | DEC10903D4BCD9119522F1F4A1A8868919FC |


|
Groovy Documentation | |||||||
| FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | METHOD | DETAIL: FIELD | METHOD |
org.vertx.java.core.TCPSupport org.vertx.groovy.core.net.NetClient org.vertx.java.core.ClientSSLSupport
@groovy.transform.CompileStatic interface NetClient extends ClientSSLSupport, TCPSupport
A TCP/SSL client.
Multiple connections to different servers can be made using the same instance.
This client supports a configurable number of connection attempts and a configurable delay between attempts.
If an instance is instantiated from an event loop then the handlers of the instance will always be called on that same event loop. If an instance is instantiated from some other arbitrary Java thread then an event loop will be assigned to the instance and used when any of its handlers are called.
Instances of this class are thread-safe
| Method Summary | |
|---|---|
void
|
close()
Close the client. |
NetClient
|
connect(int port, groovy.lang.Closure hndlr)
Attempt to open a connection to a server at the specific port and host localhost The connect is done asynchronously and on success, a NetSocket instance is supplied via the connectHandler instance |
NetClient
|
connect(int port, java.lang.String host, groovy.lang.Closure hndlr)
Attempt to open a connection to a server at the specific port and host. |
int
|
getConnectTimeout()
@return The connect timeout in milliseconds |
int
|
getReconnectAttempts()
Get the number of reconnect attempts |
long
|
getReconnectInterval()
Get the reconnect interval, in milliseconds. |
NetClient
|
setConnectTimeout(int timeout)
Set the connect timeout in milliseconds. |
NetClient
|
setReconnectAttempts(int attempts)
Set the number of reconnection attempts. |
NetClient
|
setReconnectInterval(long interval)
Set the reconnect interval, in milliseconds |
| Method Detail |
|---|
void close()
NetClient connect(int port, groovy.lang.Closure hndlr)
NetClient connect(int port, java.lang.String host, groovy.lang.Closure hndlr)
int getConnectTimeout()
int getReconnectAttempts()
long getReconnectInterval()
NetClient setConnectTimeout(int timeout)
NetClient setReconnectAttempts(int attempts)
NetClient setReconnectInterval(long interval)
Groovy Documentation


--- layout: default noindex: true title: 404 - Page Not Found ---
The file you requested could not be found!


{% extends 'template-lab-2a.html' %} {% set mainClasses = 'nhsuk-u-padding-top-0' %} {% block pageTitle %} Symptoms - NHS {% endblock %} {% block beforeContent %}
Jaundice is when your skin or the whites of your eyes turn yellow. It can be a sign of something serious, such as liver disease, so you need to get urgent medical help.
You may also have itchy skin, darker pee and paler poo than usual.
111 will tell you what to do. They can arrange a phone call from a nurse or doctor if you need one.
Go to 111.nhs.uk or call 111.
Jaundice is caused by the build-up in your body of a yellow substance called bilirubin.
There are lots of possible reasons for this and some of them are serious.
They include:
Treatment for jaundice depend on what's causing it.
Jaundice is common in newborn babies and is usually harmless.
Page last reviewed: 02 August 2019
Next review due: 02 August 2022


HyperNEAT substrate. Encapsulates substrate nodes in sets and connections. Connections can be defined explicitly or by providing mapping functions that map (connect) between nodes in sets. Node sets can be arranged as layers, however there is no limitation on node positions within the substrate - nodes in a set can be distributed throughout the substrate with no restrictions based on e.g. where nodes in other sets are located.
More...
|
| | Substrate (List< SubstrateNodeSet > nodeSetList, IActivationFunctionLibrary activationFnLibrary, int activationFnId, double weightThreshold, double maxWeight, List< SubstrateConnection > connectionList) |
| | Construct a substrate with the provided node sets and a predetermined set of connections. More...
|
| |
| | Substrate (List< SubstrateNodeSet > nodeSetList, IActivationFunctionLibrary activationFnLibrary, int activationFnId, double weightThreshold, double maxWeight, List< NodeSetMapping > nodeSetMappingList) |
| | Constructs with the provided substrate nodesets and mappings that describe how the nodesets are to be connected up. More...
|
| |
| INetworkDefinition | CreateNetworkDefinition (IBlackBox blackbox, bool lengthCppnInput) |
| | Create a network definition by querying the provided IBlackBox (typically a CPPN) with the substrate connection endpoints. More...
|
| |
|
| List< SubstrateNodeSet > | NodeSetList [get] |
| | Gets the list of substrate node sets. By convention the first nodeset describes the inputs nodes and the first node of that set describes the bias node. The last nodeset describes the output nodes. More...
|
| |
HyperNEAT substrate. Encapsulates substrate nodes in sets and connections. Connections can be defined explicitly or by providing mapping functions that map (connect) between nodes in sets. Node sets can be arranged as layers, however there is no limitation on node positions within the substrate - nodes in a set can be distributed throughout the substrate with no restrictions based on e.g. where nodes in other sets are located.
◆ Substrate() [1/2]
Construct a substrate with the provided node sets and a predetermined set of connections.
- Parameters
-
| nodeSetList | Substrate nodes, represented as distinct sets of nodes. By convention the first and second sets in the list represent the input and output noes respectively. All other sets represent hidden nodes. |
| activationFnLibrary | The activation function library allocated to the networks that are 'grown' from the substrate. |
| activationFnId | The ID of an activation function in activationFnLibrary. This is the activation function ID assigned to all nodes in networks that are 'grown' from the substrate. |
| weightThreshold | The weight threshold below which substrate connections are not created in grown networks. |
| maxWeight | Defines the weight range of grown connections (+-maxWeight). |
| connectionList | A predetermined list of substrate connections. |
◆ Substrate() [2/2]
Constructs with the provided substrate nodesets and mappings that describe how the nodesets are to be connected up.
- Parameters
-
| nodeSetList | Substrate nodes, represented as distinct sets of nodes. By convention the first and second sets in the list represent the input and output noes respectively. All other sets represent hidden nodes. |
| activationFnLibrary | The activation function library allocated to the networks that are 'grown' from the substrate. |
| activationFnId | The ID of an activation function in activationFnLibrary. This is the activation function ID assigned to all nodes in networks that are 'grown' from the substrate. |
| weightThreshold | The weight threshold below which substrate connections are not created in grown networks. |
| maxWeight | Defines the weight range of grown connections (+-maxWeight). |
///
- Parameters
-
| nodeSetMappingList | A list of mappings between node sets that defines what connections to create between substrate nodes. |
◆ CreateNetworkDefinition()
Create a network definition by querying the provided IBlackBox (typically a CPPN) with the substrate connection endpoints.
- Parameters
-
| blackbox | The HyperNEAT CPPN that defines the strength of connections between nodes on the substrate. |
| lengthCppnInput | Optionally we provide a connection length input to the CPPN. |
◆ NodeSetList
Gets the list of substrate node sets. By convention the first nodeset describes the inputs nodes and the first node of that set describes the bias node. The last nodeset describes the output nodes.
The documentation for this class was generated from the following file:
- src/SharpNeatLib/Decoders/HyperNeat/Substrate.cs


After a blissful 3h of sleep and it was already time to wake up. Our adventure started with moving out of an appartment and it ended with a moving in another one. I was starting my course/class 4 days after we landed and we still needed to move in our new apartment. (My parents were complete savior and had visited and rented in our name an apartment for us). I don’t know if you ever tried to move after an 24h international flight and 3h of sleep but you need coffee and an awful lot of motivation. We were really happy to be home and be able to see our family.
6'7" - 270lbs
The Bear
The one taking pictures and I'm always ready for
a good laugh
What is bear and bee travel?
It’s a news and tips blog about our travels and
hopefully it will help you plan your own trip! We decided to start traveling to
escape the monotony of work, trafic, lunch, sleep, repeat. We wanted to go on a long
trip to Asia for two years now, but we always pushed it back because of work
opportunities or bad timing due to our lease or reason like lets accumulate more
money, etc. We had so many good reason not to leave that we kept waiting.
And then, sitting in the parking lot of a service Ontario on a saturday morning, we
realised that we couldn’t keep pushing our dream to later for a life that we didn’t
like. (the realisation came after two really crappy months, a funeral and an
uncooperative government representative). So the next monday, we quit our jobs and 5
weeks later we were on a plane to Bangkok where our journey begins! If you wish to
contact here you can send an email to<EMAIL_ADDRESS>5'8" - 125lbs
The Bee
The one writing to you and I'm the planner
For our last day of our trip we decided to go shopping in a huge market. And for once we were not hunted by the fact that we would have to transport everything for a long period so we bought a few things! I treated myself with a new purse, the old one was in a pretty bad shape. Alexis also got a bag and we bought a vietnamiens tea set. We have a small collection of tea set so why not one more… After one last Pho it was time to leave vietnam and to begging the next step in our lives back home.


AP Top Political News at 3:02 a.m. EST
by EndPlayProtections for 660,000 immigrants on line at Supreme Court
Impeachment aside, federal budget remains a pressing matter
Meet the witnesses: Diplomats start off impeachment hearings
Impeachment witness: Ukrainians asked about holdup of aid
Democrats, GOP to vie for impeachment narrative -- on TV
GOP Rep. Peter King retiring, giving Dems new 2020 target
AP sources: Deval Patrick mulling Democratic White House run
Mulvaney to file his own impeachment lawsuit, lawyers say
Pentagon chief says Vindman should not fear Army retaliation
Prosecutors seek sentencing date for ex-Trump campaign aide


Why Join Our Club?
A wide range of locations and often unique hiking trip itineraries
Meet an extremely active group with hikes every weekend
Mountain vistas, pristine lakes, rushing streams, wild rivers
Our Adventures
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Outdoor activities
Outdoor activity is leisure pursuits engaged in the outdoors, often in natural or semi-natural settings out of town.
Learn MoreBackpacking
Backpacking may include wilderness adventures, local travel and travel to nearby counties while working from the country in which they are based.
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Mountaineering is the sport of attaining, or attempting to attain, high points in mountainous regions, mainly for the pleasure of the climb.
Learn MoreKayaking
It is distinguished from canoeing by the sitting position of the paddler and the number of blades on the paddle.
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Cycling is widely regarded as a very effective and efficient mode of transportation optimal for short to moderate distances.
Learn MoreHiking
Hiking is the preferred term for a long, vigorous walk, while the word walking is used for shorter, particularly urban walks.
Learn MoreTestimonials
In our busy world it isn’t always easy to find friends or companions to hike with. If you become a member of our club you’ll be joining what people frequently tell us one of the best hiking clubs in this country.— Nelson Smith
This club offers guided hiking and walking trips year-round - as well as wilderness camping, backpacking, cross-country skiing, and snowshoeing trips - visiting some of the wildest and natural areas.— Melina Jones


Heckmeck am Bratwurmeck
Rules
GitHub
Input player
Add player
Add AI
Start game
Grill
Rolled dice
Taken dice (value = {{ currentValue }}; {{ wormTaken ? "" : "no " }} worm taken)
Roll
Stop
Fail
Game over
{{ player.name }} ({{ getPoints(player) }} point)
New game
Unknown game state "{{state}}".
Players
{{ player.name }}
Number of pieces: {{ player.tiles.length }}
Topmost piece:
{{ player.tiles[0].number }} (
)
-


Page 1 of 1
Permanent Character Storage
Posted:
Mon Mar 20, 2006 10:05 pm
by Karl G.
What if characters, once created, could be removed from an account but never completely deleted. I was thinking maybe characters should go live in the city or something once they are made but no longer in use. Characters could be "re-activated" by us (if we want this), but this way I wouldn't have to deal with the actual deletion of memory on the server, which causes holes in the database. That would be pretty neat, eh?
Karl
Posted:
Mon Mar 20, 2006 11:36 pm
by Joe M.
yeah. here's my objection to this:
<img src="http://www.bornunseen.com/temp/chars.GIF" alt="Lots of characters!">
two months of playing xenimus.
that seems like a hell of a lot, over time and many players.
Posted:
Tue Mar 21, 2006 5:07 pm
by Karl G.
Yeah volume may be a problem, but we'll see how it goes.
Posted:
Mon Apr 24, 2006 4:03 pm
by Keats
Yeah, the only problem I could see with that would be the large volume of dead accounts.
I'm not too sure about what you meant by holes in the database, but would they pose a problem if you were to do dumps of accounts that had been inactive for x number of days? x being a large enough number to allow character re-activation if something happens clientside and that people wouldn't have their accounts deleted before they could reactivate them but small enough that server space wouldn't be at a premium.
Maybe also those characters and their names/equipment could be converted to NPC's when they get deactivated. Perhaps travelling merchants that sell whatever equipment that they had on them when their account deactivated? Then, when the dump happens, the merchant "moves on," in other words gets deleted off the server. Assuming you're even using NPC's haha


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--- title: "Lab 7: Difference in differences" linktitle: "Lab 7" date: "2020-10-14" due_date: "2020-11-16" due_time: "2:20 PM" menu: assignment: parent: Labs weight: 6 type: docs output: blogdown::html_page: toc: true editor_options: chunk_output_type: console ---
banks.dta
nsly_marijuana.dta
labtemplate.do
There are two separate parts to this lab - a set of data for working with difference-in-differences models, and another set for workin with fixed-effects models
By the end of this lab, you should be able to complete the following tasks in Stata:
Estimate and interpret difference-in-differences models
Estimate panel data models using dummy variables
Interpret panel data models
xtset
and xtreg
:The xtset
command will tell Stata that you have panel data. For example, if you have state and year data, then you would enter xtset state year
, or whatever the appropriate variable names are.
General format: xtset panelvar timevar
After declaring your panel with xtset
:
For example, something like this: xtreg income education,fe
would regress income on education, and include ﬁxed effects, where the fixed effects are the panelvar
variable you declared.
You can add fixed effects to a model more generally with the i.
prefix or areg
. A few examples:
xi: reg income i.educ i.bpl, robust
reg income i.educ i.bpl, robust
areg income i.educ,robust abosrb(bpl)
xi:
this prefix is necessary for adding i.
variables if the variables are in string form. Of course, you can use it any time! You can also use it to do fancier interactions with fixed effects, like xi: reg income i.educ*i.bpl, robust
i.var
to create a bunch of indicator variables so long as your variable is numericareg
to “absorb” a set of fixed effects - they will not be reported in your output, but they will be estimated. This method is less efficient than xtreg
becuase you’re using up degrees of freedom.This part of the lab looks at a simple difference-in-differences model based on Richardson and Troost (2009).1
Mississippi is split between two Federal Reserve Districts. During the early years of the Great Depression, the each district took a different approach to bank runs. The Sixth District increased lending,while the Eighth District responded by restricting lending to threatened banks. We look at the impact of these policies on bank survival rates using difference-in-differences.
Start a new do-file and change directory to your working directory .
In your do-file, start a log and open banks.dta
Using pencil & paper or electronic means of your choosin ( ie, you don’t need to do this in Stata), plot a graph of the number of banks in business, by district, by year. Plot number of banks in business on the y axis and the year on the x axis. Include only the years 1930 & 1931. Draw separate lines for the numbers of banks in District 6 and District 8. Draw a dotted “counterfactual” line based on your understanding of the change in bank policies. Mark all (4) actual values clearly
First, we’re going to calculate a difference-in-difference estimator by hand between 1930 and 1931. Using the browse
command, fill in \(x\) values from the following table:
| Number of banks in business | |||
|---|---|---|---|
| District | 1930 | 1931 | 1931-1930 |
| District 6 | x | x | x |
| District 8 | x | x | x |
| District 8 - District 6 | x | x | x |
What is the difference-in-difference estimator?
Now, generate a variable treat
, a binary variable equal to 1 for District 8 and 0 otherwise, and a variable post
, a binary variable
equal to 1 for the year 1931 or greater. Generate treatXpost = treat*post
.
Using the above variables, estimate the impact of looser lending restrictions on the number of banks using a difference-in-difference estimator, restricting the sample to 1930 and 1931. Write your estimates, in equation form.
Now estimate the same regression, but use all years. What is the overall impact of looser lending restrictions on bank survival? Write your estimates, in equation form.
State clearly the assumption needed to interpret these difference-in-difference estimators as causal.
Next, we’re going to look at the relationship between marijuana use and employment based on the National Longitudinal Survey of Youth 1997 Cohort (NSLY97).
Download the NSLY97 data files and save them to your working directory.
Start a new do-file and change directory to your working directory .
In your do-file, start a log and open nsly_marijuana.dta
How many individuals are in the data? How many years are they in the data?
Estimate a regression of whether marijuana use affects income, with no additional controls. Report your results in equation form.
Estimate a regression of whether marijuana use affects income, but add any controls you deem important (from the relatively limited selection I provide). How do the results change? Report your results in equation form.
One way to estimate fixed effects models is to use xtreg
with the ,fe
option. Use xtset
to tell Stata you have panel data. Then, estimate a fixed-effects regression of whether marijuana use affects income, with no additional controls. Include both individual-level and year-level fixed effects. Cluster your standard errors at the individual (\(id\)) level.
What is the coefficient on marijuana usage? What is the interpretation?
After adding fixed effects, should you include controls for gender and race/ethnicity to reduce omitted variable bias? Why or why not?
How do your results in part 9 using fixed effects compare to your results in parts 5 and 6? Why do they differ?
Name one specific factor that would create omitted variable bias in this regression.
Based on Chapter 5 of Mastering ’Metrics↩


Welcome To Fetch Inc., the canine daycare and education facility in Burnside, Dartmouth. We offer lots of experience in Doggie Daycare, Puppy Socialization and Training, Family Obedience, Agility and Rally‑O. Along with daycare services we offer many types and levels of classes and events such as seminars, obedience, agility, run thrus, and fun matches for Conformation, Obedience and Agility. We also rent out space for training, matches, meetings and seminars.
View our Classes Contact UsA tired dog is a happy dog!
 While you toil away at the office, your dog can have a great time making friends in a safe, stimulating environment under the watchful eye of our caring and attentive staff. Play times are always well-supervised and our staff are careful to provide individual attention to each of our guests.
A typical day begins with supervised play in our doggie gym followed by nap time and a snack -- then it's time to play again! We recognize the individual needs of each dog and are happy to individualize his/her program to reflect appropriate activity levels.
All dogs must be free of internal and external parasites. Proof of appropriate immunization is required in order to provide all of our visitors with a healthy environment.
Doggie Daycare is open for daycare clients from Monday 7:30 am - 6:00 pm, Tuesday - Friday 6:30am - 6:00pm.
Everything from socializing your new puppy to building a stronger bond through training, we can help!
More Details...lorem menandri ius an. Qui at assentior consequuntur. Mel vivendo nominati corrumpit et, usu ad
[yard rental blurb]
More Details...lorem menandri ius an. Qui at assentior consequuntur. Mel vivendo nominati corrumpit et, usu ad
80 Joseph Zatzman Drive, Burnside, NS | (902) 468‑0207 | fetchinc.k9@gmail.com


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Amazon Kinesis Data Analytics Application Input
The
Input
property type specifies a SQL application's streaming source, the in-application
stream name that is created, and the mapping between the two.
Input
is a property of the SqlApplicationConfiguration property.
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
JSON
{ "NamePrefix" :
String
, "InputSchema" : InputSchema, "KinesisStreamsInput" : KinesisStreamsInput, "KinesisFirehoseInput" : KinesisFirehoseInput, "InputProcessingConfiguration" : InputProcessingConfiguration, "InputParallelism" : InputParallelism }
YAML
NamePrefix:
String
InputSchema: InputSchema KinesisStreamsInput: KinesisStreamsInput KinesisFirehoseInput: KinesisFirehoseInput InputProcessingConfiguration: InputProcessingConfiguration InputParallelism: InputParallelism
Properties
NamePrefix
-
The name prefix to use when creating an in-application stream. Suppose that you specify a prefix "MyInApplicationStream." Kinesis Data Analytics then creates one or more (as per the InputParallelism count you specified) in-application streams with the names "MyInApplicationStream_001," "MyInApplicationStream_002," and so on.
Required: Yes
Type: String
Update requires: No interruption
InputSchema
-
Describes the format of the data in the streaming source, and how each data element maps to corresponding columns in the in-application stream that is being created.
Required: Yes
Type: InputSchema
Update requires: No interruption
KinesisStreamsInput
-
If the streaming source is an Amazon Kinesis data stream, identifies the stream's Amazon Resource Name (ARN).
Required: No
Type: KinesisStreamsInput
Update requires: No interruption
KinesisFirehoseInput
-
If the streaming source is an Amazon Kinesis Data Firehose delivery stream, identifies the delivery stream's ARN.
Required: No
Type: KinesisFirehoseInput
Update requires: No interruption
InputProcessingConfiguration
-
The
InputProcessingConfiguration
for the input. An input processor transforms records as they are received from the stream, before the application's SQL code executes. Currently, the only input processing configuration available isInputLambdaProcessor
.Required: No
Type: InputProcessingConfiguration
Update requires: No interruption
InputParallelism
-
Describes the number of in-application streams to create.
Required: No
Type: InputParallelism
Update requires: No interruption


---
layout: default
title: Bless Skill Tree
---
Classes
{% for class in site.data.classes %}
{% endfor %}
Skills API
I have added a JSON endpoint that will list all the classes, available skills and non-stance skills:
Skills JSON
Example skill icons and tooltips
{% for skill in site.data.paladin.non_stance_skills %}
{% assign skill_data = site.data.paladin.skills | where: "slug", skill.slug | first %}
{% include skill.html skill=skill_data %}
{% endfor %}
Example rotations
Skill rotations could be provided in a similar format to the below to help people learn the order and priority of skills for a class.
{% assign rotation = site.data.paladin.rotations | where: "name", 'Example combo' | first %}
{% for skill in rotation.skills %}
{% assign skill_data = site.data.paladin.skills | where: "slug", skill | first %}
{% include skill.html skill=skill_data %}
{% endfor %}
Example skill tree
Skill/stance trees can be put together to show the available skills in the rhythmic combat system of Bless Online.
{% assign stance = site.data.paladin.stances | first %}
{{ stance.name }}
{% assign skills = site.data.paladin[stance.slug] %}
{% for skill in skills %}
{% assign skill_data = site.data.paladin.skills | where: "slug", skill.slug | first %}
{% include skill.html skill=skill_data starter=true %}
{% include combo.html skill=skill class=site.data.paladin toplevel=true %}
{% endfor %}


--- layout: post title: "The ordered logistic distribution" permalink: /:title/ tags: [tensorflow, stan, soccer] ---
I was very pleased to wake up the other day to find that some code I wrote for the ordered logistic distribution had been accepted into TensorFlow Probability (TFP)! However when writing this post I found that my code wasn't quite right (so my testing was bad) and I've tried to rectify that in this PR which includes the code I am using in this post.
The ordered logistic distribution is one of those annoying ones which people tend to call lots of different things and also write down differently too. The implementation I used in TFP follows Stan and PyMC3 and is currently documented here.
I've decided to show an example of all this with some soccer data, in fact the same data I used in a previous post when I diagnosed a problem with the model underestimating the draw probability. I wonder if it will be the case again when we directly consider modelling the (clipped) score difference as an ordered outcome?
I'll also implement the model in TFP and Stan and see how they compare.
For a detailed explanation on the prior used on the cutpoints and general ordinal model goodness, see this case study by Michael Betancourt.
Until the next TFP release (0.10.0 I think) the ordered logistic distribution will be available in the nightly TFP release, which is tested against the TensorFlow nightly release:
import tensorflow as tf import tensorflow_probability as tfp print(f"tf: {tf.__version__}") print(f"tfp: {tfp.__version__}")
tf: 2.2.0-dev20200212 tfp: 0.10.0-dev20200214
import pandas as pd import numpy as np data_list = [] for x in range(10, 19): season = f"{x}{x+1}" url = f"http://www.football-data.co.uk/mmz4281/{season}/E0.csv" data_list.append(pd.read_csv(url)) soccer_data = ( pd.concat(data_list, axis=0) .rename( columns={ "HomeTeam": "home_team_name", "AwayTeam": "away_team_name", "FTHG": "home_goals", "FTAG": "away_goals", } ) .filter(regex="^home_|^away_") .dropna() .astype({"home_goals": np.int32, "away_goals": np.int32}) .assign( clipped_score_difference=lambda x: np.clip( x["home_goals"] - x["away_goals"], -3, 3 ) ) ) team_names = np.unique( pd.concat([soccer_data["home_team_name"], soccer_data["away_team_name"]]) ) def team_code(team_name): def fn(df): codes = pd.Categorical(df[team_name], categories=team_names).codes return codes.astype(np.int32) return fn soccer_data = soccer_data.assign( home_team=team_code("home_team_name"), away_team=team_code("away_team_name"), ) print(soccer_data.head())
home_team_name away_team_name home_goals away_goals \ 0 Aston Villa West Ham 3 0 1 Blackburn Everton 1 0 2 Bolton Fulham 0 0 3 Chelsea West Brom 6 0 4 Sunderland Birmingham 2 2 clipped_score_difference home_team away_team 0 3 1 32 1 1 3 12 2 0 5 13 3 3 10 31 4 0 27 2
num_outcomes = 7 num_teams = len(team_names) home_team = soccer_data["home_team"].to_numpy() away_team = soccer_data["away_team"].to_numpy() clipped_score_difference = soccer_data["clipped_score_difference"].to_numpy() tfb = tfp.bijectors tfd = tfp.distributions Root = tfd.JointDistributionCoroutine.Root def logit(x): return -tf.math.log(tf.math.reciprocal(x) - 1.0) def make_cutpoints(simplex): return logit(tf.math.cumsum(simplex[..., tf.newaxis, :-1], axis=-1)) def model(): cutpoints_simplex = yield Root(tfd.Dirichlet([2.0, 4.0, 6.0, 8.0, 6.0, 4.0, 2.0])) team_scale = yield Root(tfd.HalfNormal(1.0)) unit_team_strength = yield Root(tfd.MultivariateNormalDiag(loc=tf.zeros(num_teams))) team_strength = team_scale[..., tf.newaxis] * unit_team_strength home_strength = tf.gather(team_strength, home_team, axis=-1) away_strength = tf.gather(team_strength, away_team, axis=-1) clipped_score_difference = yield tfd.Independent( tfd.OrderedLogistic( cutpoints=make_cutpoints(cutpoints_simplex), loc=home_strength - away_strength, ), reinterpreted_batch_ndims=1, ) joint_dist = tfd.JointDistributionCoroutine(model)
What I like about TFP over stan is that it is automatic to sample from the prior (i.e. before we condition on any observed outcomes) and in this case it is obvious what the effect of our priors are. That is, how informative and realistic they are. Here I sample 1,000 datasets and consider the resulting clipped score differences:
import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = (12, 8) _, _, _, prior_predictive_samples = joint_dist.sample(1_000) sampled_score_difference_counts = [ np.bincount(sample, minlength=7) for sample in prior_predictive_samples ] observed_score_difference_counts = np.bincount(clipped_score_difference + 3) sampled_mean = np.mean(sampled_score_difference_counts, axis=0) sampled_quantiles = np.quantile( sampled_score_difference_counts, q=[0.025, 0.975], axis=0 ) low_error = sampled_mean - sampled_quantiles[0] high_error = sampled_quantiles[1] - sampled_mean plt.bar( x=np.arange(-3, 4), height=observed_score_difference_counts, label="observed data", color="blue", ) plt.errorbar( x=np.arange(-3, 4), y=sampled_mean, yerr=[low_error, high_error], fmt="o", label="simulated data", color="red", capsize=5, ) plt.legend(loc="upper left") plt.xlabel("clipped score difference") plt.ylabel("frequency")
The way we have written the model is quite interpretable - so we could spend some time thinking about what priors we should choose. That's not what I'm exploring in this post however - I want to see how my posterior looks!
I'm not quite sure how to decide how many chains/samples to take with TFP on the GPU right now, adding extra chains is usually cheap though so I'm running twice as many chains (and half the number of results) compared to the stan code below. I also just totally guessed the step size, I should probably check what step sizes the warmup finds to get a feel for the typical values of them.
import time as tm num_chains = 10 num_burnin_steps = 1_000 num_results = 500 def target_log_prob_fn(*state): return joint_dist.log_prob(list(state) + [clipped_score_difference + 3]) def trace_fn(states, pkr): return ( pkr.inner_results.inner_results.target_log_prob, pkr.inner_results.inner_results.leapfrogs_taken, pkr.inner_results.inner_results.has_divergence, pkr.inner_results.inner_results.energy, pkr.inner_results.inner_results.log_accept_ratio, ) def step_size_setter_fn(pkr, new_step_size): return pkr._replace( inner_results=pkr.inner_results._replace(step_size=new_step_size) ) def step_size_getter_fn(pkr): return pkr.inner_results.step_size def log_accept_prob_getter_fn(pkr): return pkr.inner_results.log_accept_ratio initial_state = list(joint_dist.sample(num_chains)[:-1]) initial_step_size = [0.25] * len(initial_state) nuts = tfp.mcmc.NoUTurnSampler(target_log_prob_fn, step_size=initial_step_size) transformed_nuts = tfp.mcmc.TransformedTransitionKernel( inner_kernel=nuts, bijector=[tfb.SoftmaxCentered(), tfb.Softplus(), tfb.Identity()], ) adaptive_transformed_nuts = tfp.mcmc.DualAveragingStepSizeAdaptation( inner_kernel=transformed_nuts, num_adaptation_steps=int(0.8 * num_burnin_steps), target_accept_prob=0.8, step_size_setter_fn=step_size_setter_fn, step_size_getter_fn=step_size_getter_fn, log_accept_prob_getter_fn=log_accept_prob_getter_fn, ) @tf.function(autograph=False, experimental_compile=False) def run_mcmc(): return tfp.mcmc.sample_chain( num_results=num_results, current_state=initial_state, num_burnin_steps=num_burnin_steps, kernel=adaptive_transformed_nuts, trace_fn=trace_fn, )
start_tfp = tm.time() samples, sample_stats = run_mcmc() end_tfp = tm.time() print(f"TFP took {end_tfp - start_tfp:.2f} seconds")
TFP took 349.02 seconds
Was unfortunate that I couldn't get experimental_compile=True
to work, which in some
examples gives a massive speed boost. It gives an error about the Gamma sampler not
being supported, which is called in the Dirichlet sampler.
As per usual I use arviz to explore the results:
import arviz as az import numpy as np sample_names = ["cutpoints_simplex", "team_scale", "unit_team_strength"] summary_vars = ["mean", "sd", "hpd_3%", "hpd_97%", "ess_bulk", "r_hat"] az_samples = { name: np.swapaxes(sample, 0, 1) for name, sample in zip(sample_names, samples) } sample_stats_names = [ "lp", "tree_size", "diverging", "energy", "mean_tree_accept", ] az_sample_stats = { name: np.swapaxes(stat, 0, 1) for name, stat in zip(sample_stats_names, sample_stats) } tfp_fit = az.from_dict( az_samples, sample_stats=az_sample_stats, coords={"teams": team_names}, dims={"unit_team_strength": ["teams"]}, ) print(az.summary(tfp_fit).filter(items=summary_vars))
mean sd hpd_3% hpd_97% ess_bulk r_hat cutpoints_simplex[0] 0.042 0.003 0.037 0.048 4994.0 1.00 cutpoints_simplex[1] 0.064 0.004 0.057 0.072 4757.0 1.00 cutpoints_simplex[2] 0.162 0.006 0.150 0.173 5277.0 1.00 cutpoints_simplex[3] 0.283 0.008 0.268 0.299 4781.0 1.00 cutpoints_simplex[4] 0.233 0.008 0.218 0.246 5265.0 1.00 cutpoints_simplex[5] 0.134 0.006 0.123 0.145 5321.0 1.00 cutpoints_simplex[6] 0.081 0.004 0.073 0.089 9180.0 1.00 team_scale 0.616 0.081 0.473 0.771 886.0 1.01 unit_team_strength[0] 1.726 0.310 1.175 2.335 985.0 1.00 unit_team_strength[1] -0.663 0.262 -1.161 -0.173 1151.0 1.01 unit_team_strength[2] -0.341 0.445 -1.174 0.487 1761.0 1.00 unit_team_strength[3] -0.562 0.355 -1.201 0.125 1764.0 1.01 unit_team_strength[4] -0.568 0.470 -1.447 0.320 2019.0 1.00 unit_team_strength[5] -0.541 0.367 -1.237 0.132 1927.0 1.00 unit_team_strength[6] -0.320 0.288 -0.874 0.218 1483.0 1.01 unit_team_strength[7] -0.479 0.357 -1.152 0.194 1607.0 1.00 unit_team_strength[8] -0.274 0.279 -0.814 0.233 1509.0 1.00 unit_team_strength[9] -1.252 0.384 -1.997 -0.572 1383.0 1.01 unit_team_strength[10] 1.782 0.314 1.158 2.338 936.0 1.00 unit_team_strength[11] -0.025 0.252 -0.473 0.485 1230.0 1.00 unit_team_strength[12] 0.729 0.246 0.272 1.188 1002.0 1.00 unit_team_strength[13] -0.565 0.280 -1.082 -0.035 1306.0 1.00 unit_team_strength[14] -1.375 0.382 -2.064 -0.616 1391.0 1.00 unit_team_strength[15] -0.724 0.324 -1.325 -0.102 1659.0 1.00 unit_team_strength[16] 0.521 0.274 0.013 1.040 1323.0 1.00 unit_team_strength[17] 1.665 0.308 1.108 2.276 986.0 1.01 unit_team_strength[18] 2.524 0.382 1.836 3.265 905.0 1.01 unit_team_strength[19] 1.779 0.315 1.205 2.369 942.0 1.00 unit_team_strength[20] -0.616 0.448 -1.433 0.245 1766.0 1.01 unit_team_strength[21] -0.143 0.240 -0.593 0.308 1160.0 1.01 unit_team_strength[22] -0.551 0.291 -1.133 -0.043 1427.0 1.01 unit_team_strength[23] -0.759 0.317 -1.329 -0.137 1557.0 1.00 unit_team_strength[24] -0.868 0.443 -1.717 -0.048 1559.0 1.00 unit_team_strength[25] 0.293 0.251 -0.176 0.756 1190.0 1.00 unit_team_strength[26] -0.100 0.232 -0.544 0.329 1062.0 1.01 unit_team_strength[27] -0.367 0.251 -0.825 0.104 1206.0 1.01 unit_team_strength[28] -0.026 0.243 -0.474 0.436 1150.0 1.00 unit_team_strength[29] 1.482 0.291 0.921 2.018 967.0 1.00 unit_team_strength[30] -0.205 0.282 -0.740 0.333 1444.0 1.00 unit_team_strength[31] -0.191 0.232 -0.605 0.271 1080.0 1.00 unit_team_strength[32] -0.013 0.241 -0.479 0.439 1199.0 1.00 unit_team_strength[33] -0.563 0.309 -1.165 -0.013 1530.0 1.01 unit_team_strength[34] -0.538 0.328 -1.169 0.056 1612.0 1.00
az.plot_forest(tfp_fit, var_names="unit_team_strength", combined=True)
Note that there is a bug in the current version of pystan with the ordered logistic distribution so we need to edit some C++ code as per this PR.
data { int<lower = 1> n; int<lower = 2> num_teams; int<lower = 2> num_outcomes; int<lower = 1, upper = num_teams> home_team[n]; int<lower = 1, upper = num_teams> away_team[n]; int<lower = 1, upper = num_outcomes> clipped_score_difference[n]; } parameters { simplex[num_outcomes] cutpoints_simplex; vector[num_teams] unit_team_strength; real<lower = 0.0> team_scale; } model { vector[num_outcomes - 1] cutpoints = logit( cumulative_sum(cutpoints_simplex[1:(num_outcomes - 1)])); vector[num_teams] team_strength = team_scale * unit_team_strength; vector[n] location = team_strength[home_team] - team_strength[away_team]; cutpoints_simplex ~ dirichlet([2.0, 4.0, 6.0, 8.0, 6.0, 4.0, 2.0]'); unit_team_strength ~ std_normal(); team_scale ~ std_normal(); clipped_score_difference ~ ordered_logistic(location, cutpoints); }
import pystan import arviz as az go_faster_flags = ["-O3", "-march=native", "-ffast-math"] stan_model = pystan.StanModel( file=stan_file, model_name="ordered_model", extra_compile_args=go_faster_flags ) stan_data = { "n": len(soccer_data), "num_teams": len(team_names), "num_outcomes": 7, "home_team": soccer_data["home_team"] + 1, "away_team": soccer_data["away_team"] + 1, "clipped_score_difference": soccer_data["clipped_score_difference"] + 4, } start_stan = tm.time() stan_fit = az.from_pystan( stan_model.sampling(data=stan_data, chains=5), coords={"team": team_names}, dims={"unit_team_strength": ["team_names"]}, ) end_stan = tm.time() print(f"stan took {end_stan - start_stan:.2f} seconds")
stan took 22.13 seconds
print(az.summary(stan_fit).filter(items=summary_vars))
mean sd hpd_3% hpd_97% ess_bulk r_hat cutpoints_simplex[0] 0.042 0.003 0.037 0.048 9153.0 1.0 cutpoints_simplex[1] 0.064 0.004 0.057 0.071 8836.0 1.0 cutpoints_simplex[2] 0.162 0.007 0.150 0.174 8737.0 1.0 cutpoints_simplex[3] 0.283 0.008 0.268 0.299 8388.0 1.0 cutpoints_simplex[4] 0.233 0.008 0.219 0.248 9650.0 1.0 cutpoints_simplex[5] 0.134 0.006 0.123 0.145 10152.0 1.0 cutpoints_simplex[6] 0.081 0.005 0.073 0.090 8664.0 1.0 unit_team_strength[0] 1.725 0.307 1.133 2.282 1015.0 1.0 unit_team_strength[1] -0.663 0.265 -1.164 -0.156 1107.0 1.0 unit_team_strength[2] -0.356 0.448 -1.170 0.499 3462.0 1.0 unit_team_strength[3] -0.564 0.365 -1.253 0.121 2398.0 1.0 unit_team_strength[4] -0.586 0.469 -1.468 0.293 3941.0 1.0 unit_team_strength[5] -0.549 0.374 -1.244 0.154 2431.0 1.0 unit_team_strength[6] -0.326 0.291 -0.901 0.182 1396.0 1.0 unit_team_strength[7] -0.477 0.350 -1.153 0.143 1982.0 1.0 unit_team_strength[8] -0.272 0.294 -0.842 0.258 1401.0 1.0 unit_team_strength[9] -1.234 0.384 -1.994 -0.550 2082.0 1.0 unit_team_strength[10] 1.781 0.310 1.194 2.353 1058.0 1.0 unit_team_strength[11] -0.019 0.261 -0.506 0.483 1102.0 1.0 unit_team_strength[12] 0.732 0.243 0.301 1.203 998.0 1.0 unit_team_strength[13] -0.565 0.279 -1.072 -0.032 1235.0 1.0 unit_team_strength[14] -1.378 0.386 -2.129 -0.655 1751.0 1.0 unit_team_strength[15] -0.722 0.328 -1.338 -0.111 1454.0 1.0 unit_team_strength[16] 0.521 0.273 0.039 1.055 1291.0 1.0 unit_team_strength[17] 1.664 0.301 1.088 2.215 1024.0 1.0 unit_team_strength[18] 2.525 0.373 1.814 3.207 1031.0 1.0 unit_team_strength[19] 1.779 0.308 1.202 2.356 1048.0 1.0 unit_team_strength[20] -0.609 0.458 -1.441 0.295 3138.0 1.0 unit_team_strength[21] -0.143 0.242 -0.615 0.295 1001.0 1.0 unit_team_strength[22] -0.550 0.293 -1.100 -0.009 1303.0 1.0 unit_team_strength[23] -0.756 0.322 -1.400 -0.184 1558.0 1.0 unit_team_strength[24] -0.859 0.449 -1.689 -0.023 3073.0 1.0 unit_team_strength[25] 0.295 0.243 -0.171 0.742 1089.0 1.0 unit_team_strength[26] -0.096 0.243 -0.525 0.387 1070.0 1.0 unit_team_strength[27] -0.366 0.252 -0.824 0.109 1067.0 1.0 unit_team_strength[28] -0.026 0.244 -0.483 0.427 1043.0 1.0 unit_team_strength[29] 1.481 0.287 0.961 2.034 995.0 1.0 unit_team_strength[30] -0.202 0.280 -0.734 0.311 1353.0 1.0 unit_team_strength[31] -0.188 0.242 -0.645 0.250 980.0 1.0 unit_team_strength[32] -0.011 0.238 -0.459 0.431 951.0 1.0 unit_team_strength[33] -0.570 0.315 -1.138 0.030 1676.0 1.0 unit_team_strength[34] -0.530 0.317 -1.089 0.091 1488.0 1.0 team_scale 0.615 0.080 0.480 0.773 1012.0 1.0
az.plot_forest(stan_fit, var_names="unit_team_strength", combined=True)
Not sure how to implemenent this without copy-pasting some of the model code, would be awesome if there is a more automatic way, like when we take prior samples. Much of this code is the same as for the prior check, execpt I had to pin the sampling operation to the CPU as my (cheap) GPU would run out of memory otherwise.
cutpoints_simplex, team_scale, unit_team_strength = samples team_strength = team_scale[..., tf.newaxis] * unit_team_strength home_strength = tf.gather(team_strength, home_team, axis=-1) away_strength = tf.gather(team_strength, away_team, axis=-1) posterior_predictive_dist = tfd.OrderedLogistic( cutpoints=make_cutpoints(cutpoints_simplex), loc=home_strength - away_strength, ) with tf.device("/CPU:0"): posterior_predictive_samples = posterior_predictive_dist.sample() sampled_score_difference_counts = [ np.bincount(sample, minlength=7) for sample in tf.reshape(posterior_predictive_samples, [-1, len(soccer_data)]) ] sampled_mean = np.mean(sampled_score_difference_counts, axis=0) sampled_quantiles = np.quantile( sampled_score_difference_counts, q=[0.025, 0.975], axis=0 ) low_error = sampled_mean - sampled_quantiles[0] high_error = sampled_quantiles[1] - sampled_mean plt.bar( x=np.arange(-3, 4), height=observed_score_difference_counts, label="observed data", color="blue", ) plt.errorbar( x=np.arange(-3, 4), y=sampled_mean, yerr=[low_error, high_error], fmt="o", label="simulated data", color="red", capsize=5, ) plt.legend(loc="upper left") plt.xlabel("clipped score difference") plt.ylabel("frequency")
So no obvious problems with the model in this test!
I've skipped over a bunch of details in the code here, but hopefully this shows how one
might start using the new OrderedLogistic
class in TFP and serves as another example
of how to run NUTS in TFP with a comparison with Stan.


It was the first viable business model for selling music online. Everybody was happy: the labels, who finally saw a way to defeat Napster; Apple, whose sales of iPod were boosted; and of course the customers, who were finally offered a seamless and legal way to acquire music. As a result, the labels agreed to let Apple extend its business, and on October 16 2003, Steve Jobs introduced the company’s second app for Windows (the first was its QuickTime Player): iTunes, “the best Windows app ever written”. Windows iPod users would finally be able to sync their device on Apple’s software, and, more importantly, every PC user could now purchase music on the iTunes Store.
It was the start of a revolution.


| DOSE: |
5 lines |
insufflated |
Cocaine |
(powder / crystals) |
My good friend and I decided we needed to try cocaine at least once, just to say we've done it. It took a long time, but eventually we were able to get some through a long line of contacts. We ended up having to buy an entire 8-ball, and spent about $120 on the thing-it was way more than we wanted, we still have alot left over.
I waited for the right occasion, and we finally broke it out, and snorted it in the bathroom at a large party. It takes very little to make a line, as we soon discovered, with the guidance of an experienced user. We started small and noticed very little effect up to ten minutes after the first line, I felt like I had maybe drank five cups of coffee-I was a little wound up. We decided to do another larger line, and this one had more of an effect.
There was a mild, pleasurable swirling feeling in my head, and I felt extremely self-confident, and talkative. For me, there was no immediate head rush or feeling of euphoria. It set in somewhat gradually and after about ten minutes I was running around the party talking to anyone and everyone. It's an excellent social drug, but I can't imagine using it long-term, or alone especially. We did three or four more lines that night, and each time we would wait until we came down, and felt depressed and irratable before snorting another one-we were very cautious.
After going on an adventure through the park, we did one final line and called it a night. Everyone agreed we had a lot of fun-we talked incessenantly to each other and to everyone we encountered. After going back to a friend's house to sleep, we all felt irratable, very pissed off, and started ranting and raving about how much cocaine sucks, and how it's not worth the money at all-we wanted to sell it all back.
We finally slept about three hours after our last line-5:00 in the morning. It is EXTREMELY difficult to sleep, and the comedown-headache is killer. It would be much more enjoyable if I could sleep as soon as I start to come down, but it kept me wide awake for the bad part.
Overall, I would use it for a social setting, like an especially large party, we kept the rest for such an occasion. I can see how it would be addictive, I liked the act of snorting it and cutting it up, and the high is really nice while it lasts-briefly, during the party, I felt ecstatic, and intense pleasure in my head. I sometimes feel like I could go for a line here or there, when I'm tired and want to go out, or when I'm particularly depressed, but I've never acted on that impulse, and it seems easy enough to control.
It's been about two and a half weeks since I first did it, and I think about it less and less as time goes on- the idea doesn't consume me. To conclude: it's nice but not worth the money for me, and arguably the comedown.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


{% load static %}
Healthcare
Doctor
{{request.user.first_name}}
Dashboard
Patient
Appointments
HOSPITAL MANAGEMENT
Logout
{% block content %} {% endblock content %}


{% extends 'base.html' %} {% block title %} New invitaion {% endblock %} {% block content %} {# Has to be post for invitation #}


Markovtsev Vadim
Web Programming @ MIPT, 2015-2016
This presentation is devoted to client-side Javascript (aka "in browser"); server-side (aka "general-purpose") JS is addressed in node.js.
Modern JS engines use just-in-time compilation to achieve better performance.
There is asm.js subset of JS, normally generated by special tools, which becomes very close to ideal (about 2x-1.5x slowdown).
Object
prototype
special attributeobj = {a: 45, "b": "test", 'c': /^abc\d+$/}
this
points to function's propertiesarguments
undefined
in case of empty returnthis
)((msg) => console.log(msg))()
a = 10;
- assign this.a
to 10
.
this
is often window
- that is, global scope.var a = 10;
- declare variable a
in function scope,
move the declaration to the beginning of the function; assign a
to 10
.let a = 10;
- declare variable a
in enclosing block scope,
move the declaration to the beginning of that block; assign a
to 10
.undefined
for (var a = 0; a < 10; a++) {}
console.log(a); // prints "10"
for (let a = 0; a < 10; a++) {}
console.log(a); // prints "undefined"
null
is very similar to undefined
null
to explicitly state the object's absence, though it should existundefined
in all other casesnew
is used to
create new objects with constructor functionsdelete
is used to
remove propertiesprototype
property is used to change all objects which have been "derived"function Car() {}
var car = new Car();
console.log(car.color); // undefined
Car.prototype.color = null;
console.log(car.color); // null
car.color = "black";
console.log(car.color); // black
class
is similar to constructor function, but with fair
semantics.extends
avoids the need of setting prototype
constructor
marks the initialization methodsuper
gives access to inherited objectstatic
makes static methods=
is for assignment== (!=)
is for equality checking with implicit type cast=== (!==)
is for equality checking without implicit type castvar a = 4, b = '4';
a == b; // true
a === b; // false
if (cond) { ... } elseif (cond) { ... }
- conditionalfor var|let i = 0; i < arr.length; i++) { ... }
- forwhile (cond) { ... }
; do { ... } while (cond)
- whileswitch (expression) {}
-
switch-case (===
is used for comparison)for var|let i in obj
- iterates over enumerable properties
of obj
in arbitrary orderfor var|let i of obj
- yields values of
iterable obj
, e.g.,
Arraylet arr = [3, 5, 7];
arr.foo = "hello";
for (let i in arr) {
console.log(i); // logs "0", "1", "2", "foo"
}
for (let i of arr) {
console.log(i); // logs "3", "5", "7"
}
undefined
.finally
always have a priority over try...catch
try {
throw "error";
} catch (err) {
return true;
} finally {
return false;
}
function* gen() {
yield 2;
yield 3;
yield 1;
}
for (let i of gen()) {
console.log(i);
}
// 2
// 3
// 1
eval()
executes
arbitrary JS code.
eval('console.log("test");');
Object.keys(obj)
returns all the properties of obj
f.toString()
returns function f
's source code'
Javascript can be awkward. There is even a dedicated collection of it's dark sides: wtfjs. Some examples taken from StackOverflow:
'5' + 3 // '53'
'5' - 3 // 2
'5'- - '3' // 8
function four() {
return
2 + 2;
}
four(); // undefined
'' == '0' // false
0 == '' // true
0 == '0' // true
false == 'false' // false
false == '0' // true
false == undefined // false
false == null // false
null == undefined // true
" \t\r\n" == 0 // true
Javascript used to be ugly; there are special projects to improve client-side programming experience, e.g. CoffeeScript.
document
and window
global variables which serve the partywindow
;
window.window === window
is truevar heading = document.createElement("h1");
heading.appendChild(document.createTextNode("Hello, World!"));
document.body.appendChild(heading);
setTimeout()
deferrs
function execution by the given interval in millisecssetInterval()
calls
function each specified number of millisecsclearTimeout()
and clearInterval()
cancel execution by identifier
returned from setTimeout()
and setInterval()
setInterval((txt) => console.log(txt), 1000, "hello, world");
There are cases when it is needed to convert an object into some text/binary representation which can be stored on disk or sent over the internet. This convertion is called serialization.
JSON.stringify()
performs the conversion object -> JSONJSON.parse()
restores object from JSONvar a = {field: 10}; // Object {field: 10}
JSON.stringify(a); // "{"field":10}"
JSON.parse(JSON.stringify(a)); // Object {field: 10}
$("#jqlist").toggle();


A portfolio for Casey's work.
Here are some things you'll need to know about me.
This is an about me section.
I am a hard working, honest individual. I am a good timekeeper, always willing to learn new skills. I am friendly, helpful and polite, have a good sense of humour. I am able to work independently in busy environments and also within a team setting. I am outgoing and tactful, and able to listen effectively when solving problems.


Building a Clojure project
What This Guide Covers
This guide covers build environment and configuration topics specific to Clojure projects. Please make sure to read our Getting Started and general build configuration guides first.
CI environment for Clojure Projects
Travis VMs currently provide
- 32-bit OpenJDK 6
- Standalone Leiningen 1.6.x.
Clojure projects on travis-ci.org assume you use Leiningen by default.
Default Test Script
Because Clojure projects on travis-ci.org assume Leiningen by default, naturally, the default command Travis CI will use to run your project test suite is
lein test
Projects that find this sufficient can use a very minimalistic .travis.yml file:
language: clojure
Using Midje on travis-ci.org
If your project uses Midje, make sure lein-midje is on your project.clj
development dependencies list and override script:
in .travis.yml
to run Midje task:
script: lein midje
Please note that for projects that only support Clojure 1.3.0 and later versions, you may need to exclude transient org.clojure/clojure
for Midje
in project.clj:
:dev-dependencies [[midje "1.3.1" :exclusions [org.clojure/clojure]]
[lein-midje "1.0.7"]])
For real world example, see Knockbox.
Dependency Management
the default command Travis CI will use to install your project dependencies is
lein deps
This will install dependencies as listed in the project.clj file.
Precompiling Java sources In Your Clojure Projects
If you need to AOT compile Java sources, for example, it is possible to override this in your .travis.yml
:
install: lein javac, deps
See general build configuration guide to learn more.
Testing Against Multiple Versions of Clojure
Leiningen has an excellent plugin called lein-multi that lets you effortlessly test against multiple versions of Clojure
(including pre-release versions like 1.4.0-beta1).
Because leiningen can run tests against any version of Clojure (not necessary the one available as clojure
in the PATH), there is no need for runtime
switchers (like RVM) for Clojure.
To use lein-multi on travis-ci.org, first install it before dependency installation and override script:
to run
lein multi test
instead of default lein test
:
language: clojure
before_install: lein plugin install lein-multi 1.1.0
script: lein multi test
For a real world example, see Monger.


{% extends "base.html" %}
{% load assets %}
{% block stylesheets %}
{% assets "css_rankings" %}
{% endassets %}
{% endblock %}
{% block scripts %}
{% assets "js_rankings" %}
{% endassets %}
{% endblock %}
{% block title %}Top player{% endblock %}
{% block content_top %}
{% include "rankings/navigation.html" %}
{% endblock %}
{% block content %}
Here you can see who is currently the top player.
| # | Nickname | EXP | K/D ratio | Kills | Headshots | Deaths | Damage given | Accuracy | Time played |
|---|---|---|---|---|---|---|---|---|---|
| {% if player.rank <= 3 %} {% else %} {{ player.rank }} {% endif %} | {{ player.username }} | {{ player.data.SGR_Account_ExperiencePointsEarned }} | {{ player.data.SGR_Account_KillDeathRatio|floatformat:4 }} | {{ player.data.SGR_Account_KillsTotal }} | {{ player.data.SGR_Account_Headshots }} | {{ player.data.SGR_Account_KilledTotal }} | {{ player.data.SGR_Account_DamageDealtTotal }} | {{ player.data.SGR_Account_HitRatio|floatformat:"2" }} % | {{ player.data.SGR_Account_TimePlayedTotalFormatted }} |
Note: Only players with a linked account are displayed on this page (more info).
{% include "rankings/date_updated.html" %} {% endblock %}


Kuew is licenced per user, plus usage.
What does that mean?
It means that you're not going to have to have a huge upfront cost if you're just a small operation, but as you grow, so can we, with you. We're going to give you some options, and you can mix and match the ones that work for you. Later on, if you need to make a change - no problem (it takes about 2 minutes).
I have ? users
you can always add more.
estimated Kuew emails
estimated Kuew sms sends
how many realtime BuzzFilters do you think you'll run
KuewUsage - how you use your Kuew
What does that mean?
Use the sliders to estimate your KuewUsage. KuewUsage is based on what we call BuzzPoints, and different things require different amounts of BuzzPoints. Bad at math - no worries. We'll let you know whats going on.
I guess I need ? BuzzPoints
estimate too low? no problem.
My monthly bill is going to be ?
Want to give us a test drive? Afraid of commitment? Spent all your money saving puppies? No problem. A pay as you go plan might work for you. Its not the best pricing, but hey, we're not gonna say no!
Take advantage of our monthly plans - better pricing, included BuzzPoints, and included features like unlimited collaborators, advanced analytics, and full data export.


It seems a bit strange to see a Bad Boys sequel in theaters in 2020. The last entry in this action film series, Bad Boys II, was released seventeen years ago, and both of its leading men (Will Smith and Martin Lawrence) are now in their fifties. However, Hollywood producers these days don’t seem to care how much time elapses between sequels, and they certainly don’t seem to mind if the actors are reaching middle age or beyond. Thus, we find ourselves with Bad Boys for Life, a film that offers all the action thrills audiences expect, which also surprisingly addresses the issue of old age.
The film wastes no time in setting up its premise: after a prison escape lifted from Silence of the Lambs, cartel boss Isabel Aretas (Kate del Castillo) sends her son Armando (Jacob Scipio) to assassinate all those responsible for her imprisonment. First on Armando’s hitlist is Detective Mike Lowrey (Smith). Lowrey survives Armando’s assassination attempt and tries to lure his former partner, Marcus Burnett (Lawrence) out of retirement to track down Armando and bring him to justice. To aid in his search for Armando, Lowrey teams up with AMMO, a police task force run entirely by younger millennial officers, to find clues that could lead to the mysterious assassin.
This may seem like a predictable and routine action movie plot, and it is. Lowrey and Burnett deliver the same gleeful police brutality and lamebrain sexist humor that they did in the previous two Bad Boys films. If you want to see Will Smith stylishly brutalize suspects and criminals alike or hear Martin Lawrence crack tired jokes about his stereotypical shrew wife, Bad Boys for Life is eager to deliver. Belgian directing duo Adil & Bilall take over for Michael Bay this go-round and present a pretty good recreation of Bay’s flashy music video style. There are big chase scenes, a plethora of gunfights, and more dramatic sweeping camera movements than you can shake a stick at.
While on the surface, Bad Boys for Life may seem like any other shoot ‘em up action movie, there’s some surprisingly thoughtful commentary on old age and dying. The movie makes no bones about the lead duo’s age; Burnett attends the birth of his grandson and decides to retire from the police force to enjoy his remaining years. This becomes a division between the titular bad boys, as Lowrey wants to continue his danger-filled, womanizing lifestyle, even into middle age. In one of the film’s few successful comedic bits, a series of match cuts crosses between Lowrey preparing for work and Burnett preparing for a day of lazing around the house. Moments like this illustrate the underlying conflict in the film: the choice of whether to accept old age or attempt to hold on to youth.
Old age is further explored from the angle of generational conflict when Lowrey teams up with the young members of the AMMO police squad. The youthful cops prefer a cautious approach to investigating, a method intended to leave behind much less collateral damage than Lowrey’s brash, guns-a-blazing approach. This causes conflict, as Lowrey’s insistence on his old-fashioned ways often sabotages his own search for Armando. While the film clearly relishes in the action-packed results of Lowrey’s excessively violent approach to police work, it’s interesting to see a film in a series dedicated to gunfights and explosions hint that perhaps gunfights and explosions aren’t the right way to solve crimes.
The discussions that Bad Boys for Life broaches regarding old age aren’t necessarily profound—this isn’t a reserved reflection on life like one might find in a quiet, somber arthouse film—but it’s interesting to see a lunkheaded action series even raise the subject of aging. The first third of the film in particular takes the time to explore these themes, even halting the action-packed plot for several minutes for the leads to discuss old age and mortality while at a bar. This scene gains even more profundity as Armand attempts to assassinate Lowrey directly afterwards. The film takes its time to communicate the impact of Lowrey’s brush with death on him and his loved ones. And although the eventual barrage of action sequences muddles these explorations, they still cast a somber tone that hovers over the whole film.
Other action movies have slowly begun to address their stars’ age. Most notably, Creed, the spinoff of the long-running Rocky franchise, includes a plot point about aging boxer Rocky Balboa’s battle with cancer. Others, like the Mission: Impossible series, the John Wick films, or the myriad Liam Neeson actioners of the 2010s are content to ignore their stars’ increasing ages. It’s not likely that Tom Cruise’s death-defying spy, Ethan Hunt, will ever discuss his fears about old age or even death. However, it seems like a small but growing number of films are willing to address the increasingly elderly elephant in the room.


25 Mar 2015
Meteora (according to my GPS at least)
After starting my journey in Thessaloniki, I went to see the famous monastries at Meteora. And by “see” I mean “not see”.
Arriving
I tried to escape the horrid weather in Thessaloniki by hopping on a coach to Trikala, the largest city in the vincinity of Meteora. Thus I squeezed myself on a bus to the coach station in the far west of the city, and managed to sit down between two middle-aged local ladies who spent most of the drive screaming at each other, not at all bothered by me sitting right in the midst of it all. At the coach station I bought a surprisingly cheap ticket (18 euros) and boarded a modern bus for the ~4-hour journey to Trikala.
As to the drive, I remember three parts: Firstly, the part where many of the cities along the way had a flair of closed-down theme parks. Empty office buildings and shops, deserted houses and a state of general disrepair. I have been told this is due to the timing, it being off-season and all, but I’m not sure I believe that explanation: Much of the forlorn atmosphere seemed to be more structural than seasonal. Many facettes of neglect looked way too long-running to be explained by simple seasonality. That’s not a theme park waiting for visitors, that’s one where the clown’s run off and the bear sold its bicycle for liquor.
Secondly, the part where I inevitably fell asleep. Something about the warmth and the rocking movement makes it impossible for me to stay awake on a bus - any mode of transport, really. So I slept for a solid two hours in what I must assume was the typical “head back & mouth wide open” position. Travel in style!
Thirdly, the part where I saw beautiful regions of northern Greece. Mountains, cliffs, winding roads and wide, open fields and lots of springtime greenery. The bus zipped along a smooth highway with sloping hills on both sides. After a few hours of that, the sun began to set. Touched by the evening sun, the entire landscape began to look every bit as green and warm as I had expected Greece to be.
Germanic invasion
Good thing though that it began to rain as soon as we reached Trikala. I managed to catch a minibus to the city centre and I also managed to get absolutely soaked on the 500m walk to my hostel. I grabbed another guest who had arrived on the same bus as me (a Canadian…ha, for once not a German!) and we got Dinner. As we got back, two new guys had arrived in our room. Germans, go figure. I remember turning to the Canadian guy and saying something along the lines of “don’t worry, we won’t be speaking German all the time…” He expressed his gratitude in fluent German. Turns out he was binational, raised speaking both languages.
If the four of us hadn’ gotten along so well, I probably would have been a bit annoyed by now. Sure, there are 81 million of us, but…come on!
Good times, bad times
The next morning the four of us shuffled to the bus stop and got our tickets to ~~Karam~~ ~~Kalam~~(Meh, I’ll look this up later) where…it was raining. On top of the rain there was also a hint of fog. Consequently, we decided to share a cab to the main monastry up on the highest hill around.
To understand my feelings about this excursion, you have to understand my reason for being in Meteora in the first place: People recommended it to me, describing the views there using words such as “stunning”, “amazing” and “mesmerizing”. A quick Google search confirmed that: Google Image search
I on the other hand would desribe the view as “what?”  The fog we had noticed at the foot of the mountain was a joke compared to the thick, nebulous wall encircling the world at the peak.
So, behold the magnificence that is impenetrable fog:
Include file ‘image.html img=”pics/IMG_20150324_110307_scaled.jpg” title=”Image” caption=”The plaza in front of the biggest monastry”’ contains invalid characters or sequences
Include file ‘image.html img=”pics/IMG_20150324_110828_scaled.jpg” title=”Image” caption=”The entrance of said monastry”’ contains invalid characters or sequences
Undaunted, the four of us walked down the road through the mountains, back to Karambaka (sic? Just google it!). Along the way we visited the second-largest monastry, which seemed to be under construction at the time.
Include file ‘image.html img=”pics/IMG_20150324_114542_scaled.jpg” title=”Image” caption=”The view down from the tower”’ contains invalid characters or sequences
Amazed, we entered the main sanctum of the monastry, a set of small rooms holding reliquies, with every inch of wall filled with intricate carvings and every inch of roof covered in paintings. As we stood there, awestruck by the opulence and the significance, a monks’ choir began to fill the air with a solemn hymn. That turned out to be played through speakers from a nearby boombox, but a good attempt nonetheless!
On our way out, we passed through a gift shop larger than the monastry itself with all kinds of Meteora-themed crap and - behold! - a pink crucifix nightlight!
Apart from the weather, we were in a rather cheerful mood, taking the overall situation with a large dose of gallows humour. Below a certain altitude, the fog cleared and even the rain died down to a drizzle. This allowed for some interesting shots of the unique rock formations in the landscape below. The positive atmosphere coupled with the rather gloomy-looking landscape led to some weirdly contrastful pictures (think Hound of Baskerville, but cheerful):
Include file ‘image.html img=”pics/IMG_20150324_125542_scaled.jpg” title=”Image” caption=”20 miles to Mordor!”’ contains invalid characters or sequences
Include file ‘image.html img=”pics/IMG_20150324_122845_scaled.jpg” title=”Image” caption=”Wet but happy enough, apparently.”’ contains invalid characters or sequences
Down in town we just so managed to catch the next bus back to Trikala. The walk from the place we ate lunch at back to hostel became a race: We were all cold and wet and we knew very well that there was only one shower at the hostel. I must have stayed in that shower until I looked like a lobster robbed of its shell. By that time, two more Germans had arrived at the hostel, surprising no one.
The rest of the day was passed in the hostel, downing coffee and reading. A pity, really, since Trikala seemed like a nice little town, but we had all just about had it with that rain.
So yes, I was in Meteora, according to the best of my knowledge. Can’t say I saw much of the place though…
Lesson 002: Leather boots are well-suited for rain. Wait, no: well-maintained leather boots are. What I’m saying is: Keep your boots well maintained, or else you might get very wet feet and also very wet boots. Leather boots dry very slowly once they are wet. If you’re traveling with only one pair of boots, you really have no excuse for not taking care of them. I’m off to buy some wax…I really hope these things are dry when I come back.
Until next time,
Arne


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If you go to any weapon with extended stats, and look at the description of the "Reload when empty" (time) It says: Reload time if your magazine is empty,
takes always
more time.
Shouldn't this be: Reload time if your magazine is empty,
always takes
more time.


We are sorry, but you must enable JavaScript to use this website.
gavin
morrow.com
The site for someone looking for interesting animations.
The site for someone looking for cool images.
The site for someone looking for fun games.


nips nips2004 nips2004-6 nips2004-6-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Bernd Fischer, Volker Roth, Jonas Grossmann, Sacha Baginsky, Wilhelm Gruissem, Franz Roos, Peter Widmayer, Joachim M. Buhmann
Abstract: De novo Sequencing of peptides is a challenging task in proteome research. While there exist reliable DNA-sequencing methods, the highthroughput de novo sequencing of proteins by mass spectrometry is still an open problem. Current approaches suffer from a lack in precision to detect mass peaks in the spectrograms. In this paper we present a novel method for de novo peptide sequencing based on a hidden Markov model. Experiments effectively demonstrate that this new method significantly outperforms standard approaches in matching quality. 1
[1] Sacha Baginsky, Mark Cieliebak, Wilhelm Gruissem, Torsten Kleffmann, Zsuzsanna Lipt´ k, Matthias M¨ ller, and Paolo Penna. Audens: A tool for automatic de novo a u peptide sequencing. Technical Report 383, ETH Zurich, Dept. of Computer Science, 2002.
[2] Ting Chen, Ming-Yang Kao, Matthew Tepel, John Rush, and George M. Church. A dynamic programming approach to de novo peptide sequencing via tandem mass spectrometry. Journal of Computational Biology, 8(3):325–337, 2001.
[3] Jimmy K. Eng, Ashley L. McCormack, and John R. Yates. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. American Society for Mass Spectrometry, 5(11):976–989, 1994.
[4] Andrew Keller, Alexey I. Nesvizhskii, Eugene Kolker, and Ruedi Aebersold. Empirical statistical model to estimate the accuracy of peptide identiﬁcations made by MS/MS and database search. Analytical Chemistry, 2002.
[5] Bin Ma, Kaizhong Zhang, Christopher Hendrie, Chengzhi Liang, Ming Li, Amanda Doherty-Kirby, and Gilles Lajoie. Peaks: Powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid Communication in Mass Spectrometry, 17(20):2337–2342, 2003.
[6] J. Alex Taylor and Richard S. Johnson. Implementation and uses of automated de novo peptide sequencing by tandem mass spectrometry. Analytical Chemistry, 73:2594– 2604, 2001.


Magister Hanellin:
Now we have no use for the blood of the platinum tribe. They no longer wish to meet with Baium. We must obtain the "blood of an angel".


Fermi Pasta Ulam: the paradox that launched scientific computing
1 In the 1950s the most
powerful computer in the world, the
MANIAC, turned to
the problem of vibrations in a lattice (as your __browser__ is now). The model
was simple---equal masses connected by nonlinear springs. (Red
masses are faster.)
[http://en.wikipedia.org/wiki/MANIAC_I, Mathematical Analyzer,
Numerical Integrator And Computer].
See the code below for the precise problem definition. Feel free
to edit the code, save, and share the new link.
2 So began a scientific
revolution---computer simulation of mathematical and physical
systems. And there was more. The system displayed *extraordinary
and unexpected behaviour*:
the FPU paradox.
Often called the
[http://en.wikipedia.org/wiki/Fermi-Pasta-Ulam_problem, FPU
__problem__], but initially regarded as a baffling paradox.
3 The researchers expected the
system's energy to "thermalize"---to (ultimately) spread evenly
among every harmonic. But, by chance, they discovered that it
didn't.
4 Instead, they found that the
initial condition repeated (somewhat) periodically. The resolution
of the paradox lead to two jewels of nonlinear theory: [b00f0,
solitons] and [http://en.wikipedia.org/wiki/Chaos_theory, chaos].
Code
Edit on left; shift+enter to compute on right.
Powered by Puzlet.


Rocket table
About
Rocket table is a lightweight table viewer for files in SAS7BDAT, SBDF, STDF and CSV formats.
Download
rocket-table-1.1.3-beta.zip
Previous versions are available as well at the
project repository
Changelog
- 2021.02.11 v1.1.3-beta - Removed "Programs" support as it was confusing everyone. Added command line parameter
to specify
type of SAS date interpretation.
- 2019.03.08 v1.1.2 - Local and shared "Programs" configs
- 2019.03.07 v1.1.1 - Upgraded to Parso v2.0.11, added "Programs" support
- 2018.07.14 v1.0.12 - Published sources
- 2018.06.08 v1.0.11 - Changed license to Apache License v2.0
- 2018.06.05 v1.0.10 - Upgraded to Parso v2.0.9
- 2017.04.05 v1.0.9 - Added encoding command line parameter
- 2017.03.19 v1.0.7 - Added support for STDF (Spotfire Text Data File) format
- 2017.03.09 v1.0.6 - Added support for SBDF (Spotfire Binary Data File) format
- 2016.04.10 v1.0.5 - Upgraded to Parso v1.2.1
- ...
- 2015.01.10 v1.0.1 - Initial version
Features
- Open files in SAS7BDAT, SBDF and CSV formats
- Real-time search over the data table
- Search using wildcard patterns
- Search by specific columns
- Show only specific set of columns
- Highlight matched rows
- Filter matched rows
- Sorting
- Truncate options
- Count total, matched, selected and unique rows
- Cross-platform (Windows, Linux, Mac OS X)
- Minimalistic flat design
- Small application size
Hotkeys
- Alt+S - focus to "Search" input
- Alt+C - focus to "Columns" input
- Ctrl+Space - autocomplete column name
- Alt+F - toggle "Filter" mode
- Alt+U - toggle "Unique" mode
- Alt+O - show "Open file" dialog
- Alt+E - show "Export table data" dialog
- Alt+T - focus to "Truncate" menu
Search wildcards
- ? - match any single character
- * - match any string (including the empty string)
Wildcard characters can be anywhere in the search string, e.g:
- word* - starts with "word"
- *word - ends with "word"
- *word* - contains "word"
Search flags
- ~ - enable case-insensitive search
- ! - inverse match
Flag characters should be at the leading positions in the string, e.g:
- !word - not equals to "word"
- ~word* - starts with "Word" or "word"
- ~!*word* - not contains "Word" or "word", or "wOrD"
Search examples
| Search string |
Match result |
| *berry |
Match all rows with berries (e.g.: Blueberry, Strawberry, etc.) |
| CMSCAT=*THERAPY |
Match all medications with category name ending with "THERAPY" (e.g.: CHEMOTHERAPY, RADIOTHERAPY and
HORMONAL THERAPY)
|
| COLUMN1=VALUE1, COLUMN2=VALUE2, COLUMN3=VALUE3 |
Match all rows where (COLUMN1=VALUE1 AND COLUMN2=VALUE2 AND COLUMN3=VALUE3) |
| SEX=Male, COUNTRY=USA, DEGREE=Academic |
Match all men from the USA who have an Academic degree |
| AGE=6?, AGE=7? |
Match all patients aged 60-70 years old (from 60 to 79) |
| COLUMN1=VALUE1, COLUMN1=VALUE2, COLUMN2=VALUE3 |
Match all rows where ((COLUMN1=VALUE1 OR COLUMN1=VALUE2) AND COLUMN2=VALUE3) |
| SEX=Male, COUNTRY=USA, COUNTRY=Canada, DEGREE=Bachelor, DEGREE=Master |
Match all men from the USA or Canada who have a Bachelor or Master degree |
| COUNTRY=!USA |
Match all patients not from the USA |
| COUNTRY=!USA, GENDER=~MaLe |
Match all men not from the USA |
Associate table files with Rocket Table in Windows
Just execute this script from the directory where the rocket-table.jar is located:
@where javaw >%TEMP%\rocket-table_javaw.var
@set /p JAVAW=<%TEMP%\rocket-table_javaw.var
@del %TEMP%\rocket-table_javaw.var
@reg add HKCU\SOFTWARE\Classes\.csv /d "rocket-table" /f
@reg add HKCU\SOFTWARE\Classes\.sas7bdat /d "rocket-table" /f
@reg add HKCU\SOFTWARE\Classes\.sbdf /d "rocket-table" /f
@reg add HKCU\SOFTWARE\Classes\.stdf /d "rocket-table" /f
@reg add HKCU\SOFTWARE\Classes\rocket-table\shell\open\command /d "\"%JAVAW%\" -jar \"%~dp0rocket-table.jar\" \"%%1\"" /f
Command line parameters
- --encoding=EncodingName - specify encoding for input files. E.g.: --encoding=Cp1250
-
--sas-date-format-type=FORMAT_TYPE - specify date format for input files.
E.g.:
- --sas-date-format-type=JAVA_DATE_LEGACY (default)
- --sas-date-format-type=SAS_FORMAT_TRIM_EXPERIMENTAL
- --sas-date-format-type=SAS_VALUE
- --sas-date-format-type=EPOCH_SECONDS
Libraries used
- Parso v2.0.14
- SBDF v6.5.0
Contacts
For questions, feature requests or technical support related to this application you can contact
<EMAIL_ADDRESS>Build from sources
- Clone or download Rocket Table repository
- Build it using maven:
mvn clean package
License
Rocket Table by Xantorohara is licensed under Apache License v2.0.
©Xantorohara, 2015-2021


Mumbai: Local Train Services on Central And Western Line to Remain Affected Today - Here's Why
Mumbai Mega Block on Sunday: Harbour line passengers are permitted to travel via Main Line and Western Railway from 10.00 am to 6.00 pm during block period.
New Delhi: Local train services, which are considered lifeline of Mumbai, will remain affected as the railways are expected to conduct mega block and jumbo block on Sunday. While the Central Railway will carry out a mega block on the main and harbour lines, a jumbo block will be condcted between Borivali and Goregaon by the Western Railway from 10:35 am to 15:35 pm .
“Due to this block, suburban trains are likely to be more crowded than usual. Passengers are requested not to take any risks while travelling. They are also requested to avoid travelling on foot board, roof top of locals and not to board extremely over crowded trains. These maintenance mega block/jumbo block are essential for infrastructure upkeep and safety”, the railways said in a statement.
List of Trains to Remain Affected on Central Line:
Down fast/semi fast services leaving CSMT from 9.53 am to 2.42 pm will be diverted on Down slow line between Matunga and Mulund stations halting at all stations between Sion and Mulund stations and will arrive destination 20 minutes behind schedule.
Dn Fast trains beyond Thane will be re-diverted on Dn fast line from Mulund.
All Up fast services leaving Thane from 10.46 am to 3.18 pm will halt at Mulund, Bhandup, Vikhroli, Ghatkopar and Kurla stations in addition to their respective scheduled halts and will arrive destination 15 minutes behind schedule.
All slow services arriving and leaving CSMT Mumbai between 11.00 am and 5.00 pm will arrive destination 15 minutes behind schedule.
List of Trains to Remain Affected on Harbour Line:
Down (Dn) Harbour line services to Vashi / Belapur /Panvel leaving CSMT Mumbai/Vadala Road from 11.34 am to 4.23 pm and Dn Harbour line services to Bandra/Goregaon leaving CSMT Mumbai from 9.56 am to 4.16 pm will remain suspended.
Up Harbour line services for CSMT Mumbai leaving Panvel/Belapur/Vashi from 9.53 am to 2.44 pm and Up Harbour line services leaving Bandra/Goregaon from 10.45 am to 4.58 pm will remain suspended.
However, Special services will run between Panvel and Kurla (Platform No.8) during the block period.
Harbour line passengers are permitted to travel via Main Line and Western Railway from 10.00 am to 6.00 pm during block period.
List of Trains to Remain Affected on Western Line:
All Up and Dn slow line trains will be worked on fast lines between Goregaon and Borivali.
Due to block , some UP and DN suburban trains will remain cancelled.During block period no trains will be dealt from platform No. 1,2,3 and 4 at Borivali station.
For breaking news and live news updates, like us on Facebook or follow us on Twitter and Instagram. Read more on India Latest News on India.com.
Story Timeline
- 17 Nov 2019 Mumbai Mega Block Today: Local Train Services on Main, Harbour Lines to Remain Affected For 5 Hours– Diversi...
- 15 Sep 2019 Mumbai: Pregnant Woman Delivers Baby Onboard a Local Train
- 15 Sep 2019 Mumbai Mega Block: Local Train Services on Central and Harbour Line to be Hit for 5 Hours | Complete Details H...
Comments - Join the Discussion


6.8. Decimal Functions and Operators
Decimal Literals
Use
DECIMAL 'xxxxxxx.yyyyyyy'
syntax to define literal of DECIMAL type.The precision of DECIMAL type for literal will be equal to number of digits in literal (including trailing and leading zeros). The scale will be equal to number of digits in fractional part (including trailing zeros).
| Example literal | Data type |
|---|---|
DECIMAL '0' |
DECIMAL(1) |
DECIMAL '12345' |
DECIMAL(5) |
DECIMAL '0000012345.1234500000' |
DECIMAL(20, 10) |
Binary Arithmetic Decimal Operators
Standard mathematical operators are supported. The table below explains precision and scale calculation rules for result. Assumingx
is of typeDECIMAL(xp, xs)
andy
is of typeDECIMAL(yp, ys)
.
| Operation | Result type precision | Result type scale |
and
|
min(38,
1 +
min(xs, ys) +
min(xp - xs, yp - ys)
)
|
max(xs, ys) |
x * y |
min(38, xp + yp) |
xs + ys |
x / y |
min(38,
xp + ys
+ max(0, ys-xs)
)
|
max(xs, ys) |
x % y |
min(xp - xs, yp - ys) +
max(xs, bs)
|
max(xs, ys) |
If the mathematical result of the operation is not exactly representable with the precision and scale of the result data type, then an exception condition is raised -
Value is out of range
.When operating on decimal types with different scale and precision, the values are first coerced to a common super type. For types near the largest representable precision (38), this can result in Value is out of range errors when one of the operands doesn’t fit in the common super type. For example, the common super type of decimal(38, 0) and decimal(38, 1) is decimal(38, 1), but certain values that fit in decimal(38, 0) cannot be represented as a decimal(38, 1).
Comparison Operators
All standard comparison operators andBETWEEN
operator work forDECIMAL
type.
Unary Decimal Operators
The-
operator performs negation. The type of result is same as type of argument.


C H E S S 2 1
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Clear
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admin password: msgdemo1
Map not visible!
To show icons, select 2D view, observe small yellow dot and zoom


PRINCESS CARABOO 1994 A film review by Timothy Voon Copyright 1997 Timothy Voon
Cast: Phoebe Cates, Wendy Hughes, Jim Broadbent, Kevin Kline, Stephen Rea, John Lithgow Director: Michael Austin Screenplay: Michael Austin and John Wells
"A princess of royal blood Caraboo she. Descended from dynasty upon dynasty of South Javanese Royalty. Who ruled the waves of the Pacific Seas; and through evil's twisted fate, our princess be captured by an evil pirate of no name. Escaped she did, by miracle's untwisted hand and thrown upon the green shores of Jerusalem (England), that unfriendly land. Bearing only the clothes she wore, with only a few silver pieces to spare the poor. So this wretched child of Caraboo our lives plead, help me, love me, mercy. Not English, nor Spanish or Hindi speak. Who is this girl full of mystery? She is Princess Caraboo." From 'The Forgotten Annals of Rare Princesses' by T.M.T. Voon.
So the question which is nicely posed to the viewer throughout the movie is - Is Princess Caraboo, who has been accepted whole heartedly into England's highest social circles, truly a princess of the bluest blood, or is she merely the greatest con artist of her time? I'll give you a clue below.
"Give me a turban, or give me a name.  I speak no English, or so I claim, but I can say 'Attoya Matoolie Estromish Zardoo', which in my language means 'God Be With You'. So throw me a party, or show me around. I know how to have a good time, all I have to do is smile. I am a prince of the truest blood, or so I claim. I am after all the prince of my heart." From 'The Forgettable Annals of Jerky Princes' by T.M.T Voon
As a film directed towards the family audience, I would say this works. Not a great fan of any film which claims to be a family film, I have to say that this film isn't bad; and since it brought smiles to my parent's faces, who give it two thumbs up, I'll refrain from being nasty.
What we have here is a tale of unprecedented imagination. Also a tale of self-belief. Like 'The Little Princess' which claims that every little girl is as special as a princess, so 'Caraboo' follows this same message to the end. For those of you who get a little sensitive over gender equality and equal opportunities, and since the rec.arts.movies.reviews seems to be overpopulated by politically conscious males, yes, every little boy can also be a prince.
Princess Feel Scale: 0% Princess Diana / *PRINCESS CARABOO* / Sleeping Beauty 100%
Timothy Voon e-mail<EMAIL_ADDRESS>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


---
layout: archive
title: Lean Prover Zulip Chat Archive
permalink: 113489newmembers/98158SubmodulesStructureVSClass.html
---
The archive URL format has changed. Please find this page at:
https://leanprover-community.github.io/archive/stream/113489-new-members/topic/Submodules.20.3A.20Structure.20VS.20Class.html


We have mices!
- monetize transparent e-business
- repurpose rich portals
- implement ubiquitous infrastructures
- repurpose wireless solutions
- integrate dynamic partnerships
- unleash clicks-and-mortar platforms
- monetize granular action-items
- reinvent robust e-commerce
- orchestrate viral content


Action
Another action
Something else here
Logout
Dashboard
Register
Register Delivery Persons
[[${user.username}]]
Logout
Error !
[[${error}]]
×
Success !
[[${success}]]
×
Username
Full Name
Phone No
Password
Add Delivery Person


Press n or j to go to the next uncovered block, b, p or k for the previous block.
| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| components/Header |
|
100% | 2/2 | 100% | 0/0 | 100% | 1/1 | 100% | 2/2 |
| components/Input |
|
63.64% | 14/22 | 83.33% | 5/6 | 44.44% | 4/9 | 61.9% | 13/21 |
| components/Loading |
|
100% | 7/7 | 100% | 3/3 | 100% | 2/2 | 100% | 7/7 |
| components/Nothing |
|
100% | 2/2 | 100% | 0/0 | 100% | 1/1 | 100% | 2/2 |
| components/OrderTableHeader |
|
100% | 8/8 | 100% | 8/8 | 100% | 4/4 | 100% | 8/8 |
| components/ValueField |
|
100% | 3/3 | 100% | 6/6 | 100% | 1/1 | 100% | 3/3 |
| hooks |
|
100% | 55/55 | 75% | 9/12 | 100% | 18/18 | 100% | 54/54 |
| pages/Auth/Home |
|
100% | 8/8 | 100% | 0/0 | 100% | 3/3 | 100% | 6/6 |
| pages/Auth/SignIn |
|
85% | 34/40 | 50% | 2/4 | 68.75% | 11/16 | 85% | 34/40 |
| pages/Auth/SignUp |
|
86.21% | 25/29 | 100% | 2/2 | 55.56% | 5/9 | 86.21% | 25/29 |
| pages/Config |
|
100% | 12/12 | 100% | 4/4 | 100% | 2/2 | 100% | 12/12 |
| pages/Config/CheckBox |
|
100% | 14/14 | 100% | 2/2 | 100% | 4/4 | 100% | 13/13 |
| pages/Dashboard |
|
100% | 25/25 | 86.36% | 19/22 | 100% | 9/9 | 100% | 22/22 |
| pages/Dashboard/Card |
|
100% | 2/2 | 100% | 2/2 | 100% | 1/1 | 100% | 2/2 |
| pages/Dividends |
|
98.33% | 59/60 | 87.5% | 7/8 | 94.74% | 18/19 | 98.31% | 58/59 |
| pages/Dividends/Card |
|
100% | 5/5 | 100% | 4/4 | 100% | 2/2 | 100% | 4/4 |
| pages/Dividends/ListDividends |
|
100% | 25/25 | 0% | 0/1 | 100% | 11/11 | 100% | 20/20 |
| pages/ListActives |
|
100% | 22/22 | 100% | 4/4 | 100% | 9/9 | 100% | 21/21 |
| pages/Performance/Actives |
|
100% | 25/25 | 100% | 8/8 | 100% | 9/9 | 100% | 22/22 |
| pages/Performance/Bonds |
|
100% | 16/16 | 100% | 4/4 | 100% | 5/5 | 100% | 14/14 |
| pages/Rentability |
|
100% | 2/2 | 100% | 0/0 | 100% | 1/1 | 100% | 2/2 |


Copyright (c) 2017 Software AG, Darmstadt, Germany and/or its licensors
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
This sample is a very simple application, deploying a trivial EPL program into a running correlator. A container runs the correlator and then runs the client tools to deploys the program.
Detailed instructions on running the sample either as a Compose-based application or via Kubernetes can be found in the README in the parent directory. If you are running via Kubernetes this sample creates the following resources which can be accessed via logs and must be deleted via delete:
* pod simple-sample
After bringing this sample up, you should notice the log from the correlator container telling you "Hello world!".


Package org.pipservices3.commons.data
package org.pipservices3.commons.data
-
ClassDescriptionCross-language implementation of dynamic object what can hold value of any type.Cross-language implementation of dynamic object array what can hold values of any type.Cross-language implementation of dynamic object map (dictionary) what can hold values of any type.DataPage<T>Data transfer object that is used to pass results of paginated queries.Data transfer object used to pass filter parameters as simple key-value pairs.Interface for data objects that contain their latest change time.Interface for data objects that are able to create their full binary copy,.Helper class to generate unique object IDs.Generic interface for data objects that can be uniquely identified by an id.Interface for data objects that have human-readable names.Interface for data objects that can be uniquely identifed by a string id.Interface for data objects that can track their changes, including logical deletion.Interface for data objects that can be versioned.An object that contains string translations for multiple languages.Data transfer object to pass paging parameters for queries.Defines projection parameters with list if fields to include into query results.Defines a field name and order used to sort query results.Defines a field name and order used to sort query results.Cross-language implementation of a map (dictionary) where all keys and values are strings.Helper class to extract and process search tags from objects.Data transfer object that is used to pass results of paginated queries.Data transfer object to pass tokenized paging parameters for queries.


--- layout: post title: Ribosome Profiling Analyses Tools (RiboFlow, RiboPy, RiboR) available on Github html: True summary: We released the first version of our ribosome profiling analyses tools. ---
Our ribosome profiling analyses tools are available on github. These include a Nextflow-based preprocessing pipeline as well as a new binary file format for organizing ribosome profiling data as a function of footprint length. This file format leverages hierarchical data format and is akin to binary file formats commonly used in bioinformatics (.bam for sequence alignments, cooler for Hi-C, etc). We also provide a complete R package (riboR), a Python interface (RiboPy) and a command line interface. For beginners, we recommend taking a look at our quick start guide.


Introductory Note : This is an important new series from Hooting Yard, in which we will endeavour to catalogue every known song, popular and otherwise, the lyrics of which mention kelp.
Number One : Sara by Bob Dylan, from the album Desire (1975), which includes the line Now the beach is deserted except for some kelp and a piece of an old ship that lies on the shore.


(+100) 345 6389
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Main Page Code but only about half of it do to length specs.
The CSS code for the main web-page.
The begining code for this web-page. Its going to be in the next update.


Paste your cargo hold below.
(This standing equates to a tax rate of {{ ctrl.taxRate | number:2 }}%, and the station's refining efficiency is {{ ctrl.reprocessingEfficiency | number:0 }}%).
You are at .
Searching market; please wait.
Getting mineral prices...
Imputed mineral prices (based on Jita {{ctrl.priceCalc}}):