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674
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Oracle Performance Tuning and Optimization
by Edward Whalen
Sams, Macmillan Computer Publishing
ISBN: 067230886x Pub Date: 04/01/96
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These qualities went into the first benchmark developed by the TPC and are still present in all the benchmarks developed today. In the years since the TPC was originally founded, it has grown from the original 8 members to over 40 of the leading computer industry companies. As of January 1, 1996, the TPC membership consists of the following companies:
| Amdahl
| Mitsubishi Electric Corp.
|
| AT&T
| Motorola
|
| Australian Dept. Admin. Svc.
| NEC Systems Laboratory
|
| Bull
| Mikkei Business Publications
|
| Compaq Computers
| Novell
|
| Convex Computer Corp.
| OKI Electric Industry
|
| Cray Research
| Olivetti S.P.A
|
| Data General Corp.
| Oracle Corp.
|
| Digital Equipment Corp.
| Pyramid Technology
|
| EDS
| Samsung
|
| EMC Corp.
| SCO
|
| Encore Computer Corp.
| Sequent Computer
|
| Fujitsu/ICL
| Siemens Nixdorf Information
|
| Hewlett-Packard
| Silicon Graphics
|
| Hitachi SW
| Software AG
|
| IBM Corp.
| Sony Corp.
|
| IDEAS International
| Stratus Computer
|
| Informix Software
| Sun Microsystems
|
| Intel Corp.
| Sybase
|
| Intergraph
| Tandem Computers
|
| ITOM International
| Toshiba Corp.
|
| Microsoft Corp.
| Tricord Systems Inc.
|
|
| Unisys Corp.
|
The TPC is made up of the general membership, in which each member company has one vote. From the general membership, a steering committee and Technical Advisory Board are elected.
The steering committee is responsible for setting the overall direction of the council and providing recommendations to the council. It is also charged with the administrative and support activities of the council.
The Technical Advisory Board (TAB) investigates issues involving compliance to TPC specifications and makes recommendations to the council on these matters. The TAB is also responsible for recommending changes to the benchmark specifications for clarity.
Any change in a specification or compliance issue decided by the TAB must be brought to the general council for decision. Every issue in the TPC requires a 2/3 majority for passage. The steering committee and TAB are merely filters; all significant decisions are made by the general council.
The TPC has gone from the original Credit/Debit benchmark to seven benchmarks (by mid-1996). These benchmarks include Decision Support, Client/Server, and Enterprise benchmarks. Each of these benchmark specifications is discussed in this chapter.
TPC Rules and Regulations
To publish a TPC benchmark result, the sponsoring company is required to follow a set of rules and regulations. These rules are designed to provide a fair playing field for all participants. Here are some of the significant rules:
- Only results that have been published can be advertised as TPC results.
- Once results are submitted to the TPC, there is a 60-day window in which the results can be challenged by other members of the council on the grounds that the benchmark does not comply with the specification. If the challenge is upheld, the results must be removed.
- Estimated results cannot be advertised.
- Comparisons cannot be made between differen
1000
t major releases of the TPC specifications (for example, you cannot compare TPC-C 3.x results with TPC-C 2.x results).
- Since January 1, 1994, all TPC benchmarks must be independently audited by a certified TPC auditor.
- Full disclosure reports must be submitted with all TPC results.
- Published results must include all primary metrics. For most TPC benchmarks, this means both performance rates and price/performance but may include more.
- TPC benchmarks support ACID properties (Atomicity, Consistency, Isolation, and Durability, as described in the following section).
- The availability date of nonshipping components must be disclosed.
These rules have been approved by the council and must be followed by all members. If these rules are violated, the council has steps it can take. It is very rare that any of the rules are violatedand then usually by mistake.
ACID Properties
The ACID (Atomicity, Consistency, Isolation, Durability) properties are designed to show that the system operates in a manner consistent with systems deployed in user installations. ACID properties are the main regulating factor that keeps benchmarks from being published in a mode inconsistent with typical operation. Consider a case in which the system might run faster if logging were disabled. Although the performance increases, the test is inconsistent with typical operation because disabling logging prohibits any recovery in the event of a system failure.
The ACID properties are as follows:
- Atomicity. The system must guarantee that all operations are atomic. This means that a transaction must complete all individual operations on the data or guarantee that partial operations have no effect on the data.
- Consistency. Consistency requires that any execution of a transaction leaves the database in a consistent state, assuming that the database is in a consistent state to begin with. This property guarantees that the books are balanced. An example of this property in the TPC-B benchmark (described later in this chapter) requires that the sum of all the account balances for a teller must match the teller balance, and that the sum of all teller balances in a branch must match the branch balance.
- Isolation. Isolation requires that operations of concurrent transactions must yield results indistinguishable from the results obtained when you execute the transactions serially in some order. This property is commonly known as serializability. Furthermore, this property must be guaranteed with respect to arbitrary transactions as well as other benchmark transactions. Repeated reads of the same records within any committed transactions must also be guaranteed to return identical data when run concurrently with any mix of arbitrary transactions.
- Durability. The durability property requires that the system must demonstrate that all transactions returned as completed have in fact been committed in spite of any system failure that may have occurred. To demonstrate this, a system crash test must be run and it must be shown that no completed transactions have been lost.
These properties guarantee the integrity of the system and add credibility to the benchmark results. Although these tests are quite time consuming, they are required for a certified result.
Requirements like these give a lot of credibility to the TPC and to the vendors that run the tests. To pass these ACID tests, the database platform must prove it is a stable, robust system capable of handling your business.
0
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Bluetooth 2.1 was desired as an ideal method of communication for the smart table due to its ubiquity and compatibility. An important technical design deviation taken was the inclusion of two Bluetooth modules rather than one. The developers decided to incorporate multiplayer gaming because the general vision of the table shifted from information to entertainment. Since a Bluetooth module can only pair with a single device, two of them are required to support two independent connections. Up to three players are possible because the microcontroller supports up to three independent UART devices through the Texas Instruments Serial Communications Interface (SCI); they are SCI-A, SCI-B, and SCI-C respectively. The first module operates on the SCI-B and the second module operates on the SCI-C. As such, the modules are connected to a total of four GPIO pins on the microcontroller; two of them are multiplexed as inputs, and two are multiplexed as outputs.
There are a couple of approaches to gathering user input: a smartphone user application or a physical remote controller. The developers chose to implement communication in the form of a smartphone application to minimize hardware production costs. The smartphone application was designed for the open-source Android operating system since it is one of the most widely used mobile platforms. The application is intended to serve as a graphical user interface through which the Smart Table can be controlled remotely. The application was written in the Java programming language and allows the user to issue commands using virtual buttons on the touch-screen display of the smartphone. The design lifecycle can be seen in the following image. The most noticeable difference between earlier versions of the app and the final version is the increased button size, as well as the pink and blue color contrast for easier visual differentiation of the buttons.
An individual simply needs to have the application on their supported Android device to communicate with the smart table. In a typical scenario, a user will download and install the Smart Table Android application. Once the app is opened, the user will connect to one of the two Bluetooth connections, broadcasted as "Player 1" and "Player 2" respectively. Upon tapping a button, the associated command is translated into packets sent over the Bluetooth frequency-hopping spreading spectrum (FHSS) and stored in a data buffer. The data is then echoed to the UART transmit pin of the Bluetooth module. The microcontroller continuously polls the UART receive pin for a command, which activates the various control flow statements in the main microcontroller program.
In the future, it would be desirable to support multiple mobile platforms such as iOS and Windows Mobile. Physical controller support would also be ideal for those who prefer it. If the developers decided to integrate their product with existing Internet-of-Things devices, such as the Amazon Echo, Philips Hue, or Nest Learning Thermostat, it would be necessary to use Wi-Fi with, or in place of, Bluetooth. Such integrations would make the smart table a more versatile and coveted product.


Setup a remote linux server, DDNS, MSI bios update
I am writing this blog to record a bad day with my new PC.
Hardware part
I have an MSI b550m mortar as a motherboard and a Ryzen 5900x as a CPU. But this motherboard bios do not support this CPU. So I need to replace it with my old Ryzen 1700x and update the bios by downloading the file from MSI’s official site and putting it in a USB root to update the motherboard from bios.
I have also accidentally removed my Win11 System EFI partition, so neither the GRUB on Ubuntu nor the boot manager from bios can wake my win11 up as it no longer possesses a boat loader.
I use a win11 iso file to enter the recovery mode and use the following command to recover this partition.
Boot your computer using the installation media or recovery disk. On the first screen.
- Enter the Command Prompt
Run the commands below to shrink a partition for
unallocated space:- diskpart - list disk - select disk # ( Select the disk where you want to add the EFI system partition.) - list partition - select partition # (Select the partition which you plan to shrink, or the place you want to create the efi partition) - shrink desired=100 (Shrink the selected partition by 100MB, you can give more if you want. If you have unallocated partition, you do not need this step)
Run the following commands to create the EFI system partition with the unallocated space.
- create partition efi size=100 (allocate the unused partition) - format quick fs=fat32 (EFI partition can only be fat32) - assign letter=S (You may replace “S” with other letters which are not already used.) - exit
Use the command below to copy the boot files from the Windows partition to the EFI system partition and create the BCD store in it:
bcdboot C:\windows /s S:
“C” is the drive letter of the system partition and “S” is the drive letter you assign to the EFI partition. But you need to check from diskpart, as your windows main partition may not on C: if you have serveral disk
Internet Setup
What is a Dynamic DNS?
Dynamic DNS allows you to direct your domain a resource that has dynamically assigned IP address. You need to creat an A (for IPv4) or AAAA (for IPv6) record on your DNS server.
How to update DDNS
As we know that the public IP address attributed by our internet operator may be changed time to time, so we need to set a client program on our host, server, or gateway that does the following:
- Detects IP address changes
- Uses the generated username and password
- Communicates the new address to the Google name servers
Google domain provides an API to perform manual updates with the API by making a POST request ot GET to the following URL:
domains.google.com/nic/update
The API requires HTTPS and here is an example request:
https://username:password@domains.google.com/nic/update?hostname=subdomain.yourdomain.com&myip=<IP_ADDRESS>
The username and password correspond to the dynamic DNS you’ve created. The hostname is the domain name you want to attribute to your IP address. Finally, you need to detect your server IP address regularly. Once you notice your IP address is changed, you can use this API to change your DDNS table.
Send request by bash
We use curl command to make an https request to google.
GOOGLE_DDNS_USERNAME="your_google_ddns_username"
GOOGLE_DDNS_PASSWORD="your_google_ddns_password"
GOOGLE_DDNS_DOMAIN="your.domain.com"
ip=""
URL="https://$GOOGLE_DDNS_USERNAME:$GOOGLE_DDNS_PASSWORD@domains.google.com/nic/update?hostname=$GOOGLE_DDNS_DOMAIN&myip=$ip"
# update the record
curl -s $URL
Detect current IP address
To get our public IP address, we need to make request to external server.
The API I use is:
curl -s https://ipinfo.io/ip
Do not disturb web servers
Now that we know the current IP address, we need to set an appropriate frequency to request the ipinfo.io and when we detect a change, we update our DDNS. The complete code is below.
google-ddns.sh
#!/bin/bash
GOOGLE_DDNS_USERNAME=""
GOOGLE_DDNS_PASSWORD=""
GOOGLE_DDNS_DOMAIN=""
# update up address
ip=$(curl -s https://ipinfo.io/ip)
FILE=./ipv4.txt
old_ip=""
if test -f "$FILE"; then
old_ip=$(cat ipv4.txt)
fi
if [ "$old_ip" != "$ip" ]; then
echo $ip > ./ipv4.txt
echo $ip
# update the record
URL="https://$GOOGLE_DDNS_USERNAME:$GOOGLE_DDNS_PASSWORD@domains.google.com/nic/update?hostname=$GOOGLE_DDNS_DOMAIN&myip=$ip"
curl -s $URL
fi
Launch the command every 30mins
We need to run the script above every 30 minutes to ensure that even if our public IP changes, we have to wait only 30 minutes to regain access to it.
I chose to use Systemd timers. Systemd is a software suite that provides an array of system components for Linux operating systems. The main aim is to unify service configuration and behaviour across Linux distributions.
systemd timer units provide a mechanism for scheduling jobs on Linux. The execution time of these jobs can be based on the time and date or on events. There is a google tutorial from “SUSE”
There are already many systemd timers that are part of system maintenance procedures that happen in the background of any Linux host. You can see then by command:
systemctl status *timer
In a nutshell, we need to create service and timer configuration files.
- google_ddns.service
- google_ddns.timer
On ubuntu these files are located in
/lib/systemd/system/
We add following files in this folder.
google_ddns.service
[Unit]
Description="Run google DDNS updater script"
[Service]
ExecStart=/your_path/google-ddns.sh
google_ddns.timer
[Unit]
Description="Run google_ddns.service 5min after boot and every 30mins relative to activation time"
[Timer]
OnBootSec=5min
OnUnitActiveSec=30min
Unit=google_ddns.service
[Install]
WantedBy=multi-user.target
Manage timers using the systemctl command
systemctl status google_ddns.timer
sudo systemctl start google_ddns.timer
sudo systemctl restart google_ddns.timer
sudo systemctl stop google_ddns.timer
Once you start the timer, you will get something like this by showing the timer status.
All articles in this blog adopt the CC BY-SA 4.0 Agreement ，Please indicate the source for reprinting！


Exchange rates today 18/11/19
Zimbabwe official and black market currency exchange rates today are as follows:
Black Market Rates:
USD to ZWL$ 21.80
USD to BOND: 15.90
Reserve Bank of Zimbabwe rate:
USD to ZWL$: 15.9788
ZWL$ to RAND: 0.9199
Zimbabwe official and black market currency exchange rates today are as follows:
Black Market Rates:
USD to ZWL$ 21.80
USD to BOND: 15.90
Reserve Bank of Zimbabwe rate:
USD to ZWL$: 15.9788
ZWL$ to RAND: 0.9199


About startup
Matrox material design multi-purpose html template. It comes with huge content and background variants. Matrox is coming very soon. We are nearly there.
Lorem ipsum dolor sit amet consectetur adipisicing elit. Id corporis mollitia vel?
Matrox material design multi-purpose html template. It comes with huge content and background variants. Matrox is coming very soon. We are nearly there.


Suggests adding Jasmine JsTestDriver adapter with compatible Jasmine library if usage of Jasmine BDD framework is found, but no Jasmine BDD framework is linked up to the project.


About MLUG
Mississauga LUG is a group of Linux users that meet once a month in a pub in Mississauga to share a beer, exchange information and talk just about anything.
We welcome everyone, from the new user to the Linux veteran, including any other Unix-like OS user.
Feel free to bring your laptop or any questions you may have. We’ll be more than glad to help you.
MLUG History
Mississauga LUG, originally known as MUMU (Mississauga Ubuntu MeetUp), started in September 2008 with a group of Ubuntu users that were looking to meet in the Mississauga area. The group wanted a laid back type of meeting, without an agenda, where users could come and talk about anything and everything and not just Linux (hence why a meet up, and not an official LUG).
For the first two years the meetings were held at Mulligan’s Pub and Grill at Dundas and Erin Mills area. And around January 2011 the meetings moved to Shoeless Joe’s at Meadowvale Town Center as most of the members lived in that area.
During the initial phase we advertised the meetings via Ubuntu’s Wiki, Forum and the CA mailing list. The Wiki has been kept the same since August 2009, with exception of the name, so you can check it to see some of the notes from them. At some point after the first few meetings a Yahoo groups page was also created, which allowed users to register and ask questions, as well access to a mailing list.
Around August 2010, and after much talk between the members, Michael registered the group as an official LUG. A domain name was voted via a poll between the members and a template layout picked during one of our meetings. And only a couple of years later, (Halloween 2012 to be more exact), the site was finally created.


Beatles album cover photographer Robert Freeman dies at 82
by Jill Lawless, The Associated PressLONDON -- Photographer Robert Freeman, who helped define the image of The Beatles with some of the band's best-known album covers, has died aged 82.
A statement on The Beatles' official website announced Freeman's death Friday but didn't give a cause.
Born in 1936, Freeman began his career as a photojournalist for London's Sunday Times and captured portraits of leading jazz musicians before working with The Beatles. He shot the black-and-white cover for the 1963 album "With The Beatles," picturing the Fab Four's faces in part-shadow. It became a defining image of the group and was used for the 1964 U.S. album "Meet The Beatles!"
In an online tribute, Paul McCartney said "people often think that the cover shot for 'Meet The Beatles' of our foreheads in half shadow was a carefully arranged studio shot."
"In fact it was taken quite quickly by Robert in the corridor of a hotel we were staying in where natural light came from the windows at the end of the corridor," McCartney wrote.
McCartney said Freeman "was one of our favourite photographers during the Beatles years who came up with some of our most iconic album covers."
He called him "imaginative and a true original thinker."
Freeman went on to photograph the covers of "Beatles For Sale," "Help!" -- with its image of the band members holding semaphore-style flags -- and "Rubber Soul." For that 1965 album Freeman subtly stretched The Beatles' faces, subtly suggesting the psychedelic experiments to come.
Ringo Starr tweeted: "God bless Robert Freeman peace and love to all his family."


8 Part II: Overview and Introduction
Data Science is a discipline that combines computing with statistics. A data analysis problem is solved in a series of data-centric steps: data acquisition and representation (Import), data cleaning (Tidy), and an iterative sequence of data transformation (Transform), data modelling (Model) and data visualization (Visualize). The end result of the process is to communicate insights obtained from the data (Communicate). Part II of this class will take you through all the steps in the process and will teach you how to approach such problems in a systematic manner. You will learn how to design data analysis pipelines as well as how to implement data analysis pipelines in R. The class will also emphasize how elegant code leads to reproducible science.
8.1 Part II: tidyverse work flow
8.2 Readings
- R4DS Chapter 1
8.3 The tidyverse workflow
First you must import your data into R. This typically means that you take data stored in a file, database, or web API, and load it into a data frame in R. If you can’t get your data into R, you can’t do data science on it!
Once you’ve imported your data, it is a good idea to tidy it. Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored. In brief, when your data is tidy, each column is a variable, and each row is an observation. Tidy data is important because the consistent structure lets you focus your struggle on questions about the data, not fighting to get the data into the right form for different functions.
Once you have tidy data, a common first step is to transform it. Transformation includes narrowing in on observations of interest (like all people in one city, or all data from the last year), creating new variables that are functions of existing variables (like computing velocity from speed and time), and calculating a set of summary statistics (like counts or means). Together, tidying and transforming are called wrangling, because getting your data in a form that’s natural to work with often feels like a fight!
Once you have tidy data with the variables you need, there are two main engines of knowledge generation: visualisation and modelling. These have complementary strengths and weaknesses so any real analysis will iterate between them many times.
Visualisation is a fundamentally human activity. A good visualisation will show you things that you did not expect, or raise new questions about the data. A good visualisation might also hint that you’re asking the wrong question, or you need to collect different data. Visualisations can surprise you, but don’t scale particularly well because they require a human to interpret them.
Models are complementary tools to visualisation. Once you have made your questions sufficiently precise, you can use a model to answer them. Models are a fundamentally mathematical or computational tool, so they generally scale well. Even when they don’t, it’s usually cheaper to buy more computers than it is to buy more brains! But every model makes assumptions, and by its very nature a model cannot question its own assumptions. That means a model cannot fundamentally surprise you.
The last step of data science is communication, an absolutely critical part of any data analysis project. It doesn’t matter how well your models and visualisation have led you to understand the data unless you can also communicate your results to others.
Surrounding all these tools is programming. Programming is a cross-cutting tool that you use in every part of the project. You don’t need to be an expert programmer to be a data scientist, but learning more about programming pays off because becoming a better programmer allows you to automate common tasks, and solve new problems with greater ease.
You’ll use these tools in every data science project, but for most projects they’re not enough. There’s a rough 80-20 rule at play; you can tackle about 80% of every project using the tools that you’ll learn in this book, but you’ll need other tools to tackle the remaining 20%. Throughout this book we’ll point you to resources where you can learn more.
8.4 The tidyverse packages
- Website: http://www.tidyverse.org/packages/
- Install with
install.packages("tidyverse")
8.5 Pipe operator
%>%
pipes an object forward into a function or call expression
- Basic piping
- x %>% f is equivalent to f(x)
- x %>% f(y) is equivalent to f(x, y)
- x %>% f %>% g %>% h is equivalent to h(g(f(x)))
- Using
%>%
with unary function calls
require(tidyverse)
## Loading required package: tidyverse
## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr
## Conflicts with tidy packages ----------------------------------------------
## filter(): dplyr, stats
## lag(): dplyr, stats
iris %>% head
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
iris %>% tail
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
- Placing lhs as the first argument in rhs call
## What are the outputs for these two lines?
3 %>% `-`(4)
iris %>% head(5)
iris %>% tail(5)
- Placing lhs elsewhere in rhs call
## What are the outputs for these
3 %>% `-`(4, .)
4 %>% c("A", "B", "C", "D", "E")[.]
More information available at: http://magrittr.tidyverse.org/
8.5.1 RStudio keyboard shortcut for %>%
- ctrl-Shift-M (Windows)
- Command-Shift-M (Mac)
8.6 Class project
For the class project for Part II, you are expected to follow the tidyverse work flow and use the tidyverse suite of packages to conduct an analytic project using data of your choice. Your project should involve these steps:
- Data importing and tidying;
- Data transformation;
- Data Visualization;
- [Optional] Data modeling;
- [Optional] Reproducible reporting.
You can use data from your current work or continue a project you have been working on. Take the feasibility of completing in a week into consideration when selecting project ideas.
If you don’t have a feasible project idea at the moment, consider continuing to analyze the bike counts data on Hawthorne Bridge and Tilikum Crossing from Part I. Daily traffic counts data for these two bridges can be found here.
Sample analytic questions are:
- After accounting for seasonal variation, is there a trend in bike traffic across these two bridges?
- How was the bike traffic affected by weather (temperature, precipitation, etc)?
- Was the bike traffic on the Hawthorne Bridge affected by the opening of Tilikum Crossing?
You can work on any one or all of these questions with visualization and/or modeling after you import, tidy and transform the data when necessary.


Review Previous Quizzes
{{chosenAttempt.name}}
Score is {{chosenAttempt.numCorrect / chosenAttempt.numberOfQuestions | percent}}
Question {{i+1}}
{{question.value}}
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{{answer.isCorrect}}


{% extends "petitions/recommendations/master_recommendationform_list.html" %} {% block title %} Master Recommendations by Date {% endblock title %} {% block recommendationstableheader %}
This table shows all the Master Recommendations and their related information of interview, hearing, admissibility and petition form done from {{startdate}} to {{enddate}} across all the prisons. You can search using any criteria
{% endblock %}


Sign In
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A collection of miscellaneous methods to simplify various tasks, including plotting, data.frame and matrix transformations, environment functions, regular expression methods, and string and logical operations, as well as numerical and statistical tools. Most of the methods are simple but useful wrappers of common base R functions, which extend S3 generics or provide default values for important parameters.
| Version: | 1.1.0 |
| Imports: | data.table, devtools, ggplot2, gridExtra, parallel, Rcpp (≥ 0.11.1), stats, xtable |
| LinkingTo: | Rcpp |
| Suggests: | knitr, rmarkdown, stringr |
| Published: | 2017-02-24 |
| Author: | Sven E. Templer |
| Maintainer: | Sven E. Templer <sven.templer at gmail.com> |
| BugReports: | https://github.com/setempler/miscset/issues |
| License: | GPL-3 |
| URL: | https://github.com/setempler/miscset |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | miscset results |
| Reference manual: | miscset.pdf |
| Vignettes: |
miscset |
| Package source: | miscset_1.1.0.tar.gz |
| Windows binaries: | r-devel: miscset_1.1.0.zip, r-release: miscset_1.1.0.zip, r-oldrel: miscset_1.1.0.zip |
| OS X binaries: | r-release: miscset_1.1.0.tgz, r-oldrel: miscset_1.1.0.tgz |
| Old sources: | miscset archive |
Please use the canonical form https://CRAN.R-project.org/package=miscset to link to this page.


 It was pretty well understood{% endraw %}
{% raw %}that Newcome intended to remove some of the stakes which{% endraw %}
{% raw %}bounded a claim belonging to Squire and Doc, and the young{% endraw %}
{% raw %}men resolved to be on the ground in time to intercept such{% endraw %}
{% raw %}irregular proceedings.{% endraw %}
{% raw %}Flag had business with a party of surveyors, which would{% endraw %}
{% raw %}take him several miles from home, and keep him out until{% endraw %}
{% raw %}nightfall, Ed declared his intention to go hunting, if, after{% endraw %}
{% raw %}eating some breakfast, he felt able to carry his gun.{% endraw %}
{% raw %}" Then you can shoot the wolves you cheated us out of last{% endraw %}
{% raw %}night," remarked Flag.{% endraw %}
{% raw %}" Confound you. Flag! I've half a mind to shoot you, or{% endraw %}
{% raw %}Doc, or whoever it was that put the strychnine on the meat,{% endraw %}
{% raw %}and then left it on the table. I'm not sure but I could make{% endraw %}
{% raw %}out a case of intentional poisoning, and have you all arrested,"{% endraw %}
{% raw %}" No, you couldn't Ed," said Doc, with a provoking smile,{% endraw %}
{% raw %}" because there wasn't any strychnine on the meat. You've{% endraw %}
{% raw %}just been cleverly sold, that's all."{% endraw %}
{% raw %}Ed glanced at the faces of Squire and Flag, and saw that{% endraw %}
{% raw %}they were on the point of " exploding." Hastily finishing his{% endraw %}
{% raw %}single cup of coffee, the victim of the " sell" arose from the{% endraw %}
{% raw %}table, took his gun from its rest, and left the shanty without{% endraw %}
{% raw %}a word.{% endraw %}
{% raw %}" Whew ! he's as mad as a hornet," said Flag. " I shouldn't{% endraw %}
{% raw %}wonder if he did something ugly in revenge."{% endraw %}
{% raw %}" Yes, wouldn't he be enraged if the joke should get into{% endraw %}
{% raw %}the papers ? His editorial dignity, and all that. He'll never{% endraw %}
{% raw %}forgive us, you may be sure."{% endraw %}


| DOSE: |
4 hits |
oral |
LSD |
(blotter / tab) |
It was Christmas Day, December 25, 2004. 11:30 at night.
I'll be Kazera. My lady'll be Keaira.
Substance - Bright, red, yellow & blue collored gecko/butterfly blotter acid (10-strip)
11:35pm - I ingest 4 hits at once, followed by 4 gulps of Deja Blu. Keaira injests 2 hits followed by 2 gulps of Deja Blu, in a BlockBuster parking lot. She's video-taping the entire night.
11:50pm - We arrive home. Immediately I run to the bathroom to urinate. I am absolutely ectsatic with anticipation, I've waited so long for this night. We light candles, put in our A Perfect Circle DVD, for visuals, and Marilyn Manson's 'Portrait of an American Family' album to help jump-start our on-set waiting period with Manson's hypnotic drum beats and twisted, aggresive melodies and vocals.
12:35am - As Keaira video-tapes me, I become unaware of what she's doing, or of what I'm doing. I was previously playing with the cd player, and somehow have found myself feeling as I'm floating. I can't seem to sit still, I feel as if I'm being lifted and swirled and the room like a tornado running rampid through an innocent Oklahoma prarie!
12:50am - Keaira is laughing frantically as she video tapes me, I seem to come up with the most humorous and unexpected dialouge. Every word I say and every glance from my eyes into hers, generates a burst of laughter and cosmic connection and understanding, thus which I never never previously experienced, through 5 lsd voyages and countless MDMA encounters.
1:00am - I find myself jumping over my living room couch. It seems so easy and natural to do. I feel invincible as if I'm no longer human but as if I'm part of every breathing, waving and sparkling entity in my home. It allows me to jump, sing and fly as if I were in a dream.
1:05am - Keaira runs from the bathroom and into the living room.. where she finds me, laid out of my coffee table having dumped and entire glass of apple juice onto my chest and lap. I couldn't give you a straight answer as to why this happened. But, I do remember I felt very in touch with my liquid state, at the time, and dumping apple juice on myself seemed like a very reasonable and profound experience at the time!
1:30am (T +2:00hrs) - This LSD experience is just starting to catch up with Keaira. She's beginning to look over her shoulder often and become more wide-eyed and spell-bound. I begin to fill sad, she was only willing to take two hits, and didn't have a clue about the incredible flowing experience I was having. It began to slightly isolate us as her trip left her under our bedroom covers, rolling around and frightened of shadows.
2:00am - I've attempted to take a cigarette break on the outside patio, but I can't even sit still for five minutes to do so! I am constantly moving, walking, striding swirling and breathing this moment. This moment which has lifted every question or concern in my existance and has slapped it in the face with a blinding light and truth which I never knew existed.
2:10am - Keaira and I make love. We have a silent moment before-hand and connect with eye-contact. We drop all of our individual desires and let our souls give-in to each other. I remember making love on 4 hits of high-powered LSD was like making love to an angel, in Heaven. We could feel each other in every cell, molecule, pore in our bodies. Every hair on the back of mjy neck stood high and tingled. Our eyes closed, our imaginations ran wild as though in this moment, we understood, as we made love, the purpose of conception of the entire universe. We were, for this moment, the center of all life and energy and love!
The next few hours resulted in Keaira and I playing with our 3 cats and mostly joking and laughing, saying silly words and rhymes and enjoying the experience as it slowly, yet swiftly faded into the horizon like the after-math of a powerful thunderstorm. Keaira never quite understood why I went so crazy, why I was humping our air-hockey table or why I couldn't sit still. She later learned the difference between 2 hits and 4 hits of this blotter. But this night, for nearly 1 year has been a night to which all other feeling, realizations and truths compare.
That night was The Landmark for my relationship with LSD. From that night forward, I fully understand it's power to completely release me from my day-in/day-out shell of routine. I cannot lie to myself about childhood or adulthood dreams. They are there, inside of us. That night helped me to realise just how exciting, energetic and loving it is to be human.
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.


Pokedex —
by
Justin Golden
<
>
Natures
Pokemon are trademarks of Nintendo, Game Freak, and The Pokémon Company
×


Component: BlinkFailC
module BlinkFailC
Implementation for BlinkFail application. Toggle the LEDs when a
Timer fires. Access out of bounds storage after a few seconds.
*
Uses
interface
Timer<TMilli> as
Timer0interface
Timer<TMilli> as
Timer1interface
Timer<TMilli> as
Timer2


The estimated rowcount on the table "
Instead of issuing a manual VACUUM ANALYZE command on this table (you can use the pgAdmin III maintenance menu for this), running VACUUM ANALYZE on a regular or automated basis should be considered. This can be achieved using a scheduler. PostgreSQL also supplies the autovacuum daemon, which will track changes made to the database and issue vacuum commands as required automatically. In most cases, autovacuum will be the best choice.
The PostgreSQL query planner makes its decision based on assumptions, taken from the estimated rowcount. If the actual rowcount is too different from the estimated rowcount, the planner might take a wrong decision, resulting in a query plan that is not optimal. This might result in poor performance.
The PostgreSQL storage needs VACUUM to have transaction IDs in tables fixed. Additionally, outdated rows from updates and deletes are not cleaned up until the VACUUM command has been issued on that table. In-depth information can be found in the online documentation, just press the help button.


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package ner
- Alphabetic
- Public
- All
Type Members
-
class
IOBTagger extends AnnotatorModel[IOBTagger] with CheckLicense with HasSimpleAnnotate[IOBTagger]
Merges token tags and NER labels from chunks in the specified format.
Merges token tags and NER labels from chunks in the specified format. For example output columns as inputs from NerConverter and Tokenizer can be used to merge.
Example
Pipeline stages are defined where NER is done. NER is converted to chunks.
val data = Seq(("A 63-year-old man presents to the hospital ...")).toDF("text") val docAssembler = new DocumentAssembler().setInputCol("text").setOutputCol("document") val sentenceDetector = new SentenceDetector().setInputCols("document").setOutputCol("sentence") val tokenizer = new Tokenizer().setInputCols("sentence").setOutputCol("token") val embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models").setOutputCol("embs") val nerModel = MedicalNerModel.pretrained("ner_jsl", "en", "clinical/models").setInputCols("sentence", "token", "embs").setOutputCol("ner") val nerConverter = new NerConverter().setInputCols("sentence", "token", "ner").setOutputCol("ner_chunk")
Define the IOB tagger, which needs tokens and chunks as input. Show results.
val iobTagger = new IOBTagger().setInputCols("token", "ner_chunk").setOutputCol("ner_label") val pipeline = new Pipeline().setStages(Array(docAssembler, sentenceDetector, tokenizer, embeddings, nerModel, nerConverter, iobTagger)) result.selectExpr("explode(ner_label) as a") .selectExpr("a.begin","a.end","a.result as chunk","a.metadata.word as word") .where("chunk!='O'").show(5, false) +-----+---+-----------+-----------+ |begin|end|chunk |word | +-----+---+-----------+-----------+ |5 |15 |B-Age |63-year-old| |17 |19 |B-Gender |man | |64 |72 |B-Modifier |recurrent | |98 |107|B-Diagnosis|cellulitis | |110 |119|B-Diagnosis|pneumonias | +-----+---+-----------+-----------+
- class MedicalNerApproach extends AnnotatorApproach[MedicalNerModel] with NerApproach[MedicalNerApproach] with Logging with ParamsAndFeaturesWritable with CheckLicense
- class MedicalNerModel extends AnnotatorModel[MedicalNerModel] with HasBatchedAnnotate[MedicalNerModel] with WriteTensorflowModel with HasStorageRef with ParamsAndFeaturesWritable with CheckLicense
- case class NamedEntityConfidence(start: Int, end: Int, entity: String, text: String, sentenceId: String, confidence: Option[Float]) extends Product with Serializable
-
class
NerChunker extends AnnotatorModel[NerChunker] with HasSimpleAnnotate[NerChunker]
Extracts phrases that fits into a known pattern using the NER tags.
Extracts phrases that fits into a known pattern using the NER tags. Useful for entity groups with neighboring tokens when there is no pretrained NER model to address certain issues. A Regex needs to be provided to extract the tokens between entities.
Example
Defining pipeline stages for NER
val data= Seq("She has cystic cyst on her kidney.").toDF("text") val documentAssembler=new DocumentAssembler() .setInputCol("text") .setOutputCol("document") val sentenceDetector=new SentenceDetector() .setInputCols("document") .setOutputCol("sentence") .setUseAbbreviations(false) val tokenizer=new Tokenizer() .setInputCols(Array("sentence")) .setOutputCol("token") val embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models") .setInputCols("sentence","token") .setOutputCol("embeddings") .setCaseSensitive(false) val ner = MedicalNerModel.pretrained("ner_radiology", "en", "clinical/models") .setInputCols("sentence","token","embeddings") .setOutputCol("ner") .setIncludeConfidence(true)
Define the NerChunker to combine to chunks
val chunker = new NerChunker() .setInputCols(Array("sentence","ner")) .setOutputCol("ner_chunk") .setRegexParsers(Array("<ImagingFindings>.*<BodyPart>")) val pipeline=new Pipeline().setStages(Array( documentAssembler, sentenceDetector, tokenizer, embeddings, ner, chunker )) val result = pipeline.fit(data).transform(data)
Show results:
result.selectExpr("explode(arrays_zip(ner.metadata , ner.result))") .selectExpr("col['0'].word as word" , "col['1'] as ner").show(truncate=false) +------+-----------------+ |word |ner | +------+-----------------+ |She |O | |has |O | |cystic|B-ImagingFindings| |cyst |I-ImagingFindings| |on |O | |her |O | |kidney|B-BodyPart | |. |O | +------+-----------------+ result.select("ner_chunk.result").show(truncate=false) +---------------------------+ |result | +---------------------------+ |[cystic cyst on her kidney]| +---------------------------+
-
class
NerConverterInternal extends AnnotatorModel[NerConverterInternal] with HasSimpleAnnotate[NerConverterInternal]
Converts a IOB or IOB2 representation of NER to a user-friendly one, by associating the tokens of recognized entities and their label.
Converts a IOB or IOB2 representation of NER to a user-friendly one, by associating the tokens of recognized entities and their label. Chunks with no associated entity (tagged "O") are filtered. See also Inside–outside–beginning (tagging) for more information.
Example
The output of a MedicalNerModel follows the Annotator schema and looks like this after the transformation.
result.selectExpr("explode(ner_result)").show(5, false) +--------------------------------------------------------------------------+ |col | +--------------------------------------------------------------------------+ |{named_entity, 3, 3, O, {word -> A, confidence -> 0.994}, []} | |{named_entity, 5, 15, B-Age, {word -> 63-year-old, confidence -> 1.0}, []}| |{named_entity, 17, 19, B-Gender, {word -> man, confidence -> 0.9858}, []} | |{named_entity, 21, 28, O, {word -> presents, confidence -> 0.9952}, []} | |{named_entity, 30, 31, O, {word -> to, confidence -> 0.7063}, []} | +--------------------------------------------------------------------------+
After the converter is used:
result.selectExpr("explode(ner_converter_result)").show(5, false) +-----------------------------------------------------------------------------------+ |col | +-----------------------------------------------------------------------------------+ |{chunk, 5, 15, 63-year-old, {entity -> Age, sentence -> 0, chunk -> 0}, []} | |{chunk, 17, 19, man, {entity -> Gender, sentence -> 0, chunk -> 1}, []} | |{chunk, 64, 72, recurrent, {entity -> Modifier, sentence -> 0, chunk -> 2}, []} | |{chunk, 98, 107, cellulitis, {entity -> Diagnosis, sentence -> 0, chunk -> 3}, []} | |{chunk, 110, 119, pneumonias, {entity -> Diagnosis, sentence -> 0, chunk -> 4}, []}| +-----------------------------------------------------------------------------------+
- See also
- trait ReadablePretrainedMedicalNer extends ParamsAndFeaturesReadable[MedicalNerModel] with HasPretrained[MedicalNerModel]
- trait ReadsMedicalNerGraph extends ParamsAndFeaturesReadable[MedicalNerModel] with ReadTensorflowModel
Value Members
- object IOBTagger extends ParamsAndFeaturesReadable[IOBTagger] with Serializable
- object MedicalNerApproach extends DefaultParamsReadable[MedicalNerApproach] with WithGraphResolver with Serializable
- object MedicalNerModel extends ReadablePretrainedMedicalNer with ReadsMedicalNerGraph with Serializable
- object NerChunker extends DefaultParamsReadable[Chunker] with Serializable
- object NerConverterInternal extends ParamsAndFeaturesReadable[NerConverterInternal] with Serializable
- object NerTaggedInternal
-
object
NerTagsEncodingInternal
Works with different NER representations as tags Supports: IOB and IOB2 https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)


Lecture 5 ggplot2 Extensions and Scatterplot Matrices (GGally)
5.1 Data
The US Bureau of Labor Statistics (BLS) conducts the American Time Use Survey (ATUS). You can download the text form of the ATUS by going to the BLS data page, finding the section labelled Spending & Time Use, then clicking on the “Text Files” button on the row for the ATUS. Or by using the following link:
https://download.bls.gov/pub/time.series/tu/
The file tu.txt
contains the documentation for the time use (tu) survey data. Section 2 of that file provides descriptions of each of the files in the pub/time.series/tu
folder. From that list we can see that tu.series
will give us a list of the available series.
5.1.1 Downloading a file from the internet
While you can manually download the files from the above URL, download.file()
lets you download files from within R. The first argument is the URL of the resource you want to download. The second argument is the destination for the file.
download.file("https://download.bls.gov/pub/time.series/tu/tu.txt", "data/tu/tu.txt")
download.file("https://download.bls.gov/pub/time.series/tu/tu.series", "data/tu/tu.series")
download.file("https://download.bls.gov/pub/time.series/tu/tu.data.0.Current", "data/tu/tu.data.0.Current")
library(readr)
series_defn <- read_tsv("data/tu/tu.series")
## Parsed with column specification:
## cols(
## .default = col_character(),
## stattype_code = col_integer(),
## sex_code = col_integer(),
## nmet_code = col_integer(),
## sjmj_code = col_integer(),
## schenr_code = col_integer(),
## work_code = col_integer(),
## elfreq_code = col_integer(),
## ecytd_code = col_integer(),
## elder_code = col_integer(),
## begin_year = col_integer(),
## end_year = col_integer()
## )
## See spec(...) for full column specifications.
series_defn
## # A tibble: 85,277 x 43
## series_id seasonal stattype_code datays_code sex_code
## <chr> <chr> <int> <chr> <int>
## 1 TUU10100AA01000007 U 10100 01 0
## 2 TUU10100AA01000013 U 10100 01 0
## 3 TUU10100AA01000014 U 10100 01 0
## 4 TUU10100AA01000015 U 10100 01 0
## 5 TUU10100AA01000018 U 10100 01 0
## 6 TUU10100AA01000019 U 10100 01 0
## 7 TUU10100AA01000025 U 10100 01 0
## 8 TUU10100AA01000035 U 10100 01 0
## 9 TUU10100AA01000036 U 10100 01 0
## 10 TUU10100AA01000037 U 10100 01 0
## # ... with 85,267 more rows, and 38 more variables: region_code <chr>,
## # lfstat_code <chr>, educ_code <chr>, maritlstat_code <chr>,
## # age_code <chr>, orig_code <chr>, race_code <chr>, mjcow_code <chr>,
## # nmet_code <int>, where_code <chr>, sjmj_code <int>,
## # timeday_code <chr>, actcode_code <chr>, industry_code <chr>,
## # occ_code <chr>, prhhchild_code <chr>, earn_code <chr>,
## # disability_code <chr>, who_code <chr>, hhnscc03_code <chr>,
## # schenr_code <int>, prownhhchild_code <chr>, work_code <int>,
## # elnum_code <chr>, ecage_code <chr>, elfreq_code <int>,
## # eldur_code <chr>, elwho_code <chr>, ecytd_code <int>,
## # elder_code <int>, lfstatw_code <chr>, pertype_code <chr>,
## # series_title <chr>, footnote_codes <chr>, begin_year <int>,
## # begin_period <chr>, end_year <int>, end_period <chr>
5.2 ggplot2 extensions
There are many extensions the community have made that build on ggplot. The following link provides a gallery of many of these extensions:
5.3 scatterplot matrix (GGally::scatmat)
One particular library, GGally, has a great set of visualizations to extend those that come prebuilt with ggplot
. One common visualization tool that is missing from ggplot is the scatterplot matrix. While base R provides splom()
in the lattice
library, GGally::ggpairs
and GGally::ggscatmat


Try it out using the test card number 4242 4242 4242 4242, a random three-digit CVC number, any expiration date in the future, and a random five-digit U.S. ZIP code.


--- layout: post status: publish published: true title: Podcast 4.54 - Retire Sammy's Sweater author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 8174 wordpress_url: http://ivyenvy.com/?p=8174 date: '2013-01-29 22:39:34 +0000' date_gmt: '2013-01-30 04:39:34 +0000' categories: - Podcast tags: - Sammy Sosa - Carlos Villanueva - Matthew Schumacher - Stadiums of Summer - Kickstarter - Lendy Castillo - Scott Hairston comments: [] ---
Andy, Corey and Kurt talk about Spring Training, Sammy Sosa and a few acquisitions. The Cubs broadcast schedule for Spring Training has been released. Six games will be broadcast on tv and if you have an internet connection, you'll be able to listen to every game.
Sammy Sosa can't/won't stay out of the spotlight and publicized his opinion that the Cubs should retire his #21. We asked our listeners how they felt about this through a poll on our Facebook page and the results were 83% voting "no" and 17% voting "yes". The three of us are in agreement that Sammy's number should not be retired and we discuss the reasons for our opinions.
The Cubs have reportedly signed Scott Hairston and we discuss what this means for the outfielders at the Cubs disposal. The Carlos Villanueva deal is official and Lendy Castillo is DFA'd to make room.
We play an interview that I did with Matthew Schumacher. Matthew has a Kickstarter campaign underway and you can support his project here - Stadiums of Summer. In the interview, Matthew describes his project and his motivation. We'd encourage you to go check his project out, watch his video and support a fellow life-long Cubs fan.
We wrap things up with the Cubs.com Internet Muscle Flex of the Week and the Caption Contest.
This episode of the Ivy Envy Podcast was brought to you by Stitcher Smart Radio. Download the free app by searching for it in the app store and be sure and enter "Relentless" in the promo code box for a chance to win $100.


I and a good friend of mine had decided to get some amphetamine to celebrate 16th of May (day before 17th, which is when Norway got its constitution, which again means lots of people in the streets wearing traditional national-costumes and kids with icecream). We had discussed the possibility of doing speed the day before, both of us wanted to, but we both concluded with that it was best not to do it. The next day however, after the ephedra started working with us, we slowly reconsidered, and ended up talking each other into doing it anyway. We became hungry for a more intense speedy experience than my last ephedra caps could give us alone.
I arrived at my friend's place and I had brought abt. 10 caps of ephedrine, and 12 big brown tablets of guarana. We took 4 and 5 each respectively. After a while we kind of ended up talking each other into getting some speed for the rest of the night. My friend called this guy we know but he didn't answer. A bit later we were getting talkative and I had slight electric buzzes going up and down my spine. He asked me to call as he was afraid our man didn't want to talk to him. I hesitated because I didn't feel I knew him all that well, but less so than I would have otherwise.
I called him and he was going to come over with 1/2 gram for us. This would be the second time I tried speed, the first being new years eve 2000->2001. He finally arrived after a few more calls and apparent delays. We were quite talkative by now, and I couldn't sit still. I talked a lot and made jokes and participated a lot more than I would have normally. We waited untill he left to snort the white powder now in our posession.
We went outside to go to town and see what was happening, a lot of partying is the order of the day on this date all over the nation. We decided to go to the place we were most likely to meet people we knew. We got there walking rather briskly, i was definately feeling the speed good by now. Lots of people, and we were rather put off because everything seemed much less fun than we had though it would be. I was quite surprised at this, as last time I tried amphetamine I was able to enjoy pretty much anything. Everybody seemed like they would to me on acid.
We went upstairs, and this big guy looked at me, smiled, and started touching me all over, I wrestled out of his grip and continued upwards, sligthy unnerved by being (sexually) harassed by a man (me being male myself). My friend told me the guy did, or had done, a lot of drugs, and that my pupils were huge, he probably just wanted to mindfuck with my obviously intoxicated brain.
Nothing of interest happening here we trawled all the other places, but it was the same everywhere. Boring. We ended up going back home to my friends place, and spent the night talking. I had an undefinable feeling of not being content with the situation (obviously wanting to have fun). We ended up being remarkably indifferent towards everything. After a while it became a running joke that this was the funniest speed-comedown we had ever had (my friend had done speed maybe a dozen times, I only two). This became the only thing we were able to laugh at. Everything else seemed unimportant and quite uninteresting.
We witnessed the city waking up, and when we heard the orchestras playing in the streets we decided it was time to go outside. Slighly paranoid we went for a walk in the forest. As we arrived at the edge we saw a police-car approaching and I got a bit nervous as quite a few hash-smoking juveniles used this forest as a hideout (ourselves included when we were younger). I turned around after walking 100 metres up the path into the forest and the police car had stopped. We discussed what to do. i was supposed to leave the talking to my friend as I am easily intimidated by, and don't feel comfortable in the presence of cops while intoxicated (or on a comedown). i doubt if that was a very wise decicion, but luckily we never met any authority figures.
It was hard coming up with anything to talk about, everything loosing its interesting properties. We sat on a bench for a while basically just panting (exhaustion), experiencing melancholia and slight despair over our quite incomfortable situation. We were however quite indifferent about it, and that was the only thing we were laughing about. We went home, avoiding a few police officers, and I was so lucky to find out my parents were not home, so I confidently took a cab home and was able to perish the uncomfortableness by showering, eating and talking to my sister a bit. Got the best night of sleep in a long time.
It seems that the negative effects of the ephedra was magnified significantly by the amphetamine, but the negative effects of amphetamine were abolished by the ephedrine. Might sound like a contradiction or pure nonsense, but my ephedra comedowns are usually mildly pessimistic affairs where I get a bit emotionally upset and discontent. As for speed-comedowns, my only pure one so far was spent laughing while the rest of the room sat silently looking glum. Not a very typical reaction I am told, and this time I seemed to project this feeling over to my friend, although magnified by the speed (or so I imagine). We were held in a limbo, always on the edge of speed euphoria and ephedra melancholia.
I will stick to ephedra, I was only interested in experiencing amphetamine to see what it was like, and I don't think it is worth it alone. In combination with alcohol it is quite enjoyable though, as I experienced virtally no comedown and felt excellent the next day, but that is better left for another report...
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.


Jenny
Lee
Conflicts
080915
The most recent conflict I could remember was at work a few weeks ago at the restaurant. It was a somewhat busy night, which means as a server, I could make great money if I worked hard.
As the night progressed, I realized at a point that I had less tables than certain servers, and even though it was my section's tables that were getting flipped, my tables kept going to other servers. This made me frustrated, because in a system where the hostesses were in control of the amount of tables I could have and in turn make money from, I felt like I was losing not only potential money, but also wasting my time.
It was after another few rounds of getting skipped when I decided to confront the hostesses. I asked them if they were forgetting to seat me or if there was something else happening that they were planning. Their only answer was that there was a miscommunication issue, to which I felt extremely annoyed.
I did not want to cause harm to our relationship over a mistake, so I did hold in most of my frustrations while telling them why I was feeling unhappy at the moment. I am glad that I did not go beyond that even though the events at the restaurant that night were all adding stress to that already stressful evening. They then apologized and it really seemed like they understood why I could be a bit upset at the situation. This made me feel better and I was glad that we were able to get over the conflict without any hard feelings or regrettable actions.
From this, I think I learned that patience can really help you calm down at a heated moment and even prevent conflict altogether. And if a conflict were to occur, I learned that explaining to the other person why I was upset really helps them understand my frustrations. If I were to go back and change something, I would probably have either confronted them earlier about the situation and asked them about it in a casual way, just as a checkup. This could have probably prevented some long explanations and frustrations built up.


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Overview of changes from Automation 3
From Aegisub Manual
To put it short, the changes from Automation 3 to Automation 4 are huge, almost everything has been completely redesigned and rewritten. You should however find that many things still work the same, and the rest work better, hopefully. Automation 3 scripts can still run with little or no modifications, see the moving from Automation 3 page for details on that.
Architectural changes
These are the changes in how the scripting engine(s) integrate with Aegisub, ie. how scripts are loaded and managed and not as much what happens when they are running.
Actually, what is called Automation 4 is only the architecture described here. When you use Automation 4 to run scripts written in Lua (and are not Automation 3 scripts) it's called Automation 4 Lua.
The most obvious change is the support for multiple scripting engines. Automation 3 was built around only supporting Lua, Automation 4 has been designed to support scripts written in all kinds of languages as long as a scripting engine has been implemented into it. Implementing a new scripting engine consists of implementing about 5-8 C++ classes and quite possibly a bunch more helpers. The basic implementation should be a few day's work and then an uncountable number of hours for documentation, standard includes and support. If you are interested in implementing support for a new scripting language please contact the Aegisub team.
Automation 4 now supports "plugging in" script functionality in several different places. Each different kind of functionality a script can support is called a "feature". Currently this really only means the export filters supported by Automation 3 and then macros. Export filters appear in the Export dialogue and are intended for large batch-operations. Macros appear in the Automation menu and are intended for smaller tasks. A third, still not fully drafted feature, is support for scripting file format reading and writing, to be able to import and export all the most exotic subtitle file formats in existence.
Macro features can retrieve the current selection in the subtitle grid, modify the subtitles in-line and change the selection upon return. They can also set one or more Undo points. Last but not least, they can show dialogue boxes to get more information from the user.
Finally, one script file can provide more than one feature, also more than one of each kind of supported feature. A script file can also provide no features at all, though that's not very useful.
The Lua scripting engine
The probably single biggest and most important change from Automation 3 to Automation 4 Lua is that the Lua version has been upgraded from 5.0 to 5.1. While there are a large number of changes between the two versions, the perhaps most important for writing Automation 4 Lua scripts is the modulo operator that has finally been introduced into the language. You can see more details at the Lua homepage.
The rest of the changes to Lua scripting interface can best be summed up as: Give as much control as possible to the script and don't do any unrequested data generation.
In more detail, these are the main points of the changes from Automation 3 to Automation 4 Lua. They are discussed in more detail below.
- No more a fixed entry point, script entry points are instead defined with
register_xxx
functions - Direct manipulation of the subtitle file rather than manipulating a large Lua table
- Much more direct control over the entire execution process
- Various changes in the data structures used
- More complete Unicode support in include files
- More advanced layout calculations in karaskel include file
You no longer need to name your functions anything specific, rather you tell Aegisub what your entry point functions are called. What you do is register each feature you want, using the aegisub.register_macro
and aegisub.register_filter
functions. (Exception: If you use the karaoke skeleton functions you do need to follow some specific naming schemes and should not register any features.)
Instead of getting a huge table with all lines in the subtitle file, Automation 4 Lua provides your script with a "user data object" you can use to get, save and replace lines in the subtitle file with. (This object is usually called the subs object.) Using this subs object you can not only get the dialogue lines in the subtitle file, but also manipulate the style lines and the Script Info section. On the other hand, you are no longer given a styles or meta table directly, but the karaskel include has a function to extract that information.
As a result of the direct manipulation of the subtitle file you can now do, you also have much more control over what happens when during script execution. Automation 3 always split the script execution up in three phases: Generate input, run processing, read output. Using the subs object you can instead intermix those three phases freely, or just do as usual. This is especially convenient for macros, since they only need to touch the lines they actually modify, rather than needing to import the entire file and having it re-read by Aegisub when done. The skeletons provided by karaskel.lua can to an extent automate managing the execution process.
The data structures used have also been changed to better support the more complete subtitle file representation and to better reflect the philosophy of doing as little as possible before requested. For example, the karaoke data tables that were an inherent part of the line data structure in Automation 3 are no longer automatically generated, instead you use a separate function to have them generated. If you use karaskel, you will however not need to worry about that. A couple of new fields have also been introduced to support the changes in the ASS2 format and some preparations are made to support further evolutions of the ASS format, such as ASS3 and AS5. The actual changes are too many to list here.
A new unicode.lua include file has been introduced. This file provides a much more clean and usable interface for manipulating UTF-8 encoded text than the old next_utf_char
and utf_len
functions.
Last but not least, the text layout algorithms used in karaskel have been improved a lot and now, among other things, support sub-pixel positioned text, proper handling of inter-syllable spaces and an advanced furigana layout algorithm. The furigana layout algorithm also produces data structures so similar to syllable structures you can in effect use exactly the same code to produce effects for both main syllables and furigana.
| Overview: |
Automation Manager • Running macros • Using export filters • Standard macros • Changes from Automation 3 • Moving from Automation 3 |
|---|---|
| Karaoke Templater reference: |
Declaring templates • Execution order • Modifiers • Inline-variables ($-variables) • Code lines and blocks • Execution envirionment |
| Lua reference: |
Registration • Subtitles object • Progress reporting • Config dialogues • Misc. APIs • karaskel.lua • utils.lua • unicode.lua • cleantags.lua |
| Karaskel concepts: |
Style tables • Dialogue line tables • Syllable tables • Inline effects • Furigana |


It doesn’t take much to get a JavaScript web framework like Vue.Js to work with a Buffalo backend.
In this short video we’ll look at just how simple it is to hook up Vue.js to a Buffalo application that is serving JSON.
Source: https://github.com/gobuffalo/vuerecipe


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{Young}ist is always looking for fresh perspectives, new ideas, and different ways to support our grassroots, innovative youth culture.
We want to know how you experience and explore art, we want your savvy commentary on popular culture, current events, intersectional oppressions – and most importantly, we want to support ourselves and each other (which is why we pay our writers $50-$65 per article!). For some outstanding examples of published pieces that demonstrate on-point youth analysis on pop culture and politics, reflective narratives on Millennial identity and struggle, and introspective angles on youth and/or student organizing for social justice, please click here to get an idea on the content we are looking for.
Want to get a sense of our beats? Most of our content sits at the intersection of youth and...


Slack plunges 11% after Microsoft's rival messaging platform extends its lead with 20 million daily active users
by finanzen.net GmbH- Microsoft reported Tuesday that Teams, a messaging competitor to Slack, has 20 million daily active users.
- Slack plunged as much as 11% on the news. In October, Slack reported 12 million daily active users.
- In addition to having more daily active users than Slack, Microsoft also said that Teams users grew by more than 50% since its last report in July.
- Watch Slack trade live on Markets Insider.
Shares of Slack fell as much as 11% Tuesday after Microsoft reported that its competing messaging product, Teams, has 20 million daily active users.
That's 8 million more users than Slack, which reported 12 million daily active users in October.
It's also a huge gain in the lead Microsoft already had over Slack. In July, the company announced that it had 13 million daily active users. That means that Microsoft has seen usage of Teams increase more than 50% in the last few months. Microsoft shares were relatively unchanged Tuesday.
Slack, on the other hand, has argued that while it doesn't have as many daily active users, the ones that do use the application spend more than nine hours a day using it, showing that people actually like using the product.
The company has also previously called out Microsoft for its business practices, calling it "surprisingly unsportsmanlike." Microsoft offers Teams for free to businesses that use Microsoft Office 365, which could mean that it's counting users that don't actually use Teams very often, Slack CEO Stewart Butterfield has claimed.
Slack is down more than 45% since spiking nearly 50% during its first day of public trading in June.
Slack has a consensus price target of $29.65 and nine "buy" ratings, 8 "hold" ratings, and two "sell" ratings from analysts covering the company, according to Bloomberg data.


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|
mlpack
1.0.12
|
Performs the MST calculation using the Dual-Tree Boruvka algorithm, using any type of tree. More...
Classes | |
| struct | SortEdgesHelper |
| For sorting the edge list after the computation. More... | |
Public Member Functions | |
| DualTreeBoruvka (const typename TreeType::Mat &dataset, const bool naive=false, const MetricType metric=MetricType()) | |
| Create the tree from the given dataset. More... | |
| DualTreeBoruvka (TreeType *tree, const typename TreeType::Mat &dataset, const MetricType metric=MetricType()) | |
| Create the DualTreeBoruvka object with an already initialized tree. More... | |
| ~DualTreeBoruvka () | |
| Delete the tree, if it was created inside the object. More... | |
| void | ComputeMST (arma::mat &results) |
| Iteratively find the nearest neighbor of each component until the MST is complete. More... | |
| std::string | ToString () const |
| Returns a string representation of this object. More... | |
Private Member Functions | |
| void | AddAllEdges () |
| Adds all the edges found in one iteration to the list of neighbors. More... | |
| void | AddEdge (const size_t e1, const size_t e2, const double distance) |
| Adds a single edge to the edge list. More... | |
| void | Cleanup () |
| The values stored in the tree must be reset on each iteration. More... | |
| void | CleanupHelper (TreeType *tree) |
| This function resets the values in the nodes of the tree nearest neighbor distance, and checks for fully connected nodes. More... | |
| void | EmitResults (arma::mat &results) |
| Unpermute the edge list and output it to results. More... | |
Private Attributes | |
| UnionFind | connections |
| Connections. More... | |
| const TreeType::Mat & | data |
| Reference to the data (this is what should be used for accessing data). More... | |
| TreeType::Mat | dataCopy |
| Copy of the data (if necessary). More... | |
| std::vector< EdgePair > | edges |
| Edges. More... | |
| MetricType | metric |
| The instantiated metric. More... | |
| bool | naive |
| Indicates whether or not O(n^2) naive mode will be used. More... | |
| arma::vec | neighborsDistances |
| List of edge distances. More... | |
| arma::Col< size_t > | neighborsInComponent |
| List of edge nodes. More... | |
| arma::Col< size_t > | neighborsOutComponent |
| List of edge nodes. More... | |
| std::vector< size_t > | oldFromNew |
| Permutations of points during tree building. More... | |
| bool | ownTree |
| Indicates whether or not we "own" the tree. More... | |
| struct mlpack::emst::DualTreeBoruvka::SortEdgesHelper | SortFun |
| double | totalDist |
| Total distance of the tree. More... | |
| TreeType * | tree |
| Pointer to the root of the tree. More... | |
Performs the MST calculation using the Dual-Tree Boruvka algorithm, using any type of tree.
For more information on the algorithm, see the following citation:
General usage of this class might be like this:
More advanced usage of the class can use different types of trees, pass in an already-built tree, or compute the MST using the O(n^2) naive algorithm.
| MetricType | The metric to use. IMPORTANT: this hasn't really been tested with anything other than the L2 metric, so user beware. Note that the tree type needs to compute bounds using the same metric as the type specified here. |
| TreeType | Type of tree to use. Should use DTBStat as a statistic. |
| mlpack::emst::DualTreeBoruvka< MetricType, TreeType >::DualTreeBoruvka | ( | const typename TreeType::Mat & | dataset, |
| const bool | naive = false , |
||
| const MetricType | metric = MetricType() |
||
| ) |
Create the tree from the given dataset.
This copies the dataset to an internal copy, because tree-building modifies the dataset.
| data | Dataset to build a tree for. |
| naive | Whether the computation should be done in O(n^2) naive mode. |
| leafSize | The leaf size to be used during tree construction. |
| mlpack::emst::DualTreeBoruvka< MetricType, TreeType >::DualTreeBoruvka | ( | TreeType * | tree, |
| const typename TreeType::Mat & | dataset, | ||
| const MetricType | metric = MetricType() |
||
| ) |
Create the DualTreeBoruvka object with an already initialized tree.
This will not copy the dataset, and can save a little processing power. Naive mode is not available as an option for this constructor; instead, to run naive computation, construct a tree with all the points in one leaf (i.e. leafSize = number of points).
| tree | Pre-built tree. |
| dataset | Dataset corresponding to the pre-built tree. |
| mlpack::emst::DualTreeBoruvka< MetricType, TreeType >::~DualTreeBoruvka | ( | ) |
Delete the tree, if it was created inside the object.
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private |
Adds all the edges found in one iteration to the list of neighbors.
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private |
Adds a single edge to the edge list.
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private |
The values stored in the tree must be reset on each iteration.
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private |
This function resets the values in the nodes of the tree nearest neighbor distance, and checks for fully connected nodes.
| void mlpack::emst::DualTreeBoruvka< MetricType, TreeType >::ComputeMST | ( | arma::mat & | results | ) |
Iteratively find the nearest neighbor of each component until the MST is complete.
The results will be a 3xN matrix (with N equal to the number of edges in the minimum spanning tree). The first row will contain the lesser index of the edge; the second row will contain the greater index of the edge; and the third row will contain the distance between the two edges.
| results | Matrix which results will be stored in. |
|
private |
Unpermute the edge list and output it to results.
| std::string mlpack::emst::DualTreeBoruvka< MetricType, TreeType >::ToString | ( | ) | const |
Returns a string representation of this object.
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The Mathematics Program seeks to give students an understanding and appreciation of the beauty and utility of mathematics. Quantitative and analytic skills are increasingly important in biology, economics, and the social sciences, as well as in engineering and the physical sciences.
| Faculty Names | Pictures |
|---|---|
| Joel Kilty | |
| Michael Lamar | |
| Ellen Swanson |


ASM v6.0 : a small and fast bytecode manipulation framework.
This is repackaged from "org.ow2.asm" in ASM v6.0.
The only changes in the code were to avoid compiler warnings (e.g to MethodVisitor.visitMethodInsn to comment out the "deprecated"), and fix javadocs error.


RavenDB Connection
Database connections are generally defined at either the application or request instance levels. The RavenDB client is designed for the former, and configuring its IDocumentStore
type as a singleton is the recommended implementation, which we will do for our applications. In the process of ensuring that we can properly configure this instance, we will also have to address the concepts of configuration and dependency injection (or, in the case of Cinco, its replacement). You can review the all the code at the checkpoint for step 3.
A Bit about RavenDB
RavenDB is a document database written in C# that allows the us to store our Plain Old CLR Objects (AKA POCOs) as documents. It uses Json.NET to serialize POCOs into JSON documents, which are then stored by the server. It stores data in "databases", with documents grouped together in "collections" based on their Id. There are no schemas for these documents, or within a collection; however, in practice, most documents in a collection will have a similar shape. Documents can be indexed, to allow fast retrieval or even alternate forms of the data. It provides its own query language (RQL), based on Lucene, and also exposes most all its features in LINQ syntax off the document collection.
It also supports multi-node clusters, and a lot of advanced features we won't be tapping for this project. We will use a single instance of RavenDB, and a single database within it, for all our data. RavenDB will run for seven days without a license installed, but they offer free developer and community licenses at their site.
All Implementations
Each of our implementations will allow for the following user-configurable options:
Url
- The URL and port of the RavenDB serverDatabase
- The database to use for queries, so that we do not need to specify one for each call
(If you decide to go live with this, you'll end up generating a client certificate and needing at least a community license; we'll address this at the end of the project.)
Additionally, we will write start-up code that ensures our document collections are properly indexed. A collection does not have to "exist" before defining an index, so we can create the indexes on a database without any documents. We will create the appropriate database through the Raven Studio rather than through code, so that we can set it as the default when we initialize our IDocumentStore
instance. We do not need to create indexes to be able to retrieve individual documents by Id; we will, though, need to address the following scenarios:
- Category retrieval by web log Id (used to generate lists of available categories)
- Category retrieval by web log Id and slug (used to retrieve details for category archive pages)
- Comment retrieval by post Id (used to retrieve comments for a post)
- Page retrieval by web log Id (used to retrieve a list of pages)
- Page retrieval by web log Id and permalink (used to fulfill single page requests)
- Post retrieval by web log Id (used many places)
- Post retrieval by web log Id and permalink (used to fufill single post requests)
- Post retrieval by web log Id and category Id (used to get posts for category archive pages)
- Post retrieval by web log Id and tag (used to get posts for tag archive pages)
- User retrieval by e-mail address and password (used for log on)
One interesting feature that distinguishes RavenDB indexes from relational indexes is that if you index multiple fields, you can utilize the index for any of those fields, not just the first one listed. This means that we will only need to create one index on the Category
collection, for example, because we have one requirement by web log Id, and one by web log Id and slug. A single index on both fields will support both queries.
Individual Implementations
Each of these not only evolves from step 2 to step 3, they also evolve as Uno moves to Cinco. It may help your understanding to read each of them, even if your interest is just in one of them.
Uno - In Depth
Dos - In Depth
Tres - In Depth
Quatro - In Depth
Cinco - In Depth


A reading from the Book of the Prophet Daniel
In those days: Some men came by agreement and found
Daniel making petition and supplication before his God. Then they came near and
said before the king, concerning the interdict, "O king! Did you not sign
an interdict, that any man who makes petition to any god or man within thirty
days except to you, O king, shall be cast into the den of lions?" The king
answered, "The thing stands fast, according to the law of the Medes and
Persians, which cannot be revoked." Then they answered before the king,
"That Daniel, who is one of the exiles from Judah, pays no heed to you, O king,
or the interdict you have signed, but makes his petition three times a
day." Then the king, when he heard these words, was much distressed, and
set his mind to deliver Daniel; and he laboured till the sun went down to
rescue him. Then these men came by agreement to the king, and said to the king,
"Know, O king, that it is a law of the Medes and Persians that no
interdict or ordinance which the king establishes can be changed." Then
the king commanded, and Daniel was brought and cast into the den of lions. The
king said to Daniel, "May your God, whom you serve continually, deliver
you!" And a stone was brought and laid upon the mouth of the den, and the
king sealed it with his own signet and with the signet of his lords, that
nothing might be changed concerning Daniel. Then the king went to his palace,
and spent the night fasting; no diversions were brought to him, and sleep fled
from him. Then, at break of day, the king arose and went in haste to the den of
lions. When he came near to the den where Daniel was, he cried out in a tone of
anguish and said to Daniel, "O Daniel, servant of the living God, has your
God, whom you serve continually, been able to deliver you from the lions?"
Then Daniel said to the king, "O king, live for ever!  Then the king was exceedingly glad, and commanded that Daniel be
taken up out of the den. So Daniel was taken up out of the den, and no kind of
hurt was found upon him, because he had trusted in his God. And the king
commanded, and those men who had accused Daniel were brought and cast into the
den of lions--they, their children, and their wives; and before they reached
the bottom of the den the lions overpowered them and broke all their bones in
pieces. Then King Darius wrote to all the peoples, nations, and languages that
dwell in all the earth: "Peace be multiplied to
you. I make a decree, that in all my royal dominion men tremble and fear before
the God of Daniel, for he is the living God, enduring for ever; his kingdom
shall never be destroyed, and his dominion shall be to the end. He delivers and
rescues, he works signs and wonders in heaven and on earth, he who has saved
Daniel from the power of the lions."
The word of the Lord
RESPONSORIAL PSALM
Daniel 3:<IP_ADDRESS>.48.51.52 (R. 46b)
R. Sing praise to him and highly exalt him for ever.
Bless the Lord, dews and snows, sing praise to him
and highly exalt him for ever.
R. Sing praise to him and highly exalt him for ever.
Bless the Lord, ice and cold, sing praise to him
and highly exalt him for ever.
R. Sing praise to him and highly exalt him for ever.
Bless the Lord, frosts and snows, sing praise to
him and highly exalt him for ever.
R. Sing praise to him and highly exalt him for ever.
Bless the Lord, nights and days, sing praise to him
and highly exalt him for ever.
R. Sing praise to him and highly exalt him for ever.
Bless the Lord, light and darkness, sing praise to
him and highly exalt him for ever.
R. Sing praise to him and highly exalt him for ever.
Bless the Lord, lightnings
and clouds, sing praise to him and highly exalt him for ever.
R. Sing praise to him and highly exalt him for ever.
Let the earth bless the Lord; let it sing praise to
him and highly exalt him for ever.
R. Sing praise to him and highly exalt him for ever.
(Gospel Acclamation)
Year 2
FIRST READING Revelation
18:1-2.21-23; 19:1-3.9
“Fallen is Babylon the great!”
A reading from the Book of Revelation
I, John, saw another
angel coming down from heaven, having great authority; and the earth was made
bright with his splendour. And he called out with a
mighty voice, "Fallen, fallen is Babylon
the great!  Then
a mighty angel took up a stone like a great millstone and threw it into the
sea, saying, "So shall Babylon the great city be thrown down with
violence, and shall be found no more; and the sound of harpers and minstrels,
of flute players and trumpeters, shall be heard in you no more; and a craftsman
of any craft shall be found in you no more; and the sound of the millstone
shall be heard in you no more; and the light of a lamp shall shine in you no
more; and the voice of bridegroom and bride shall be heard in you no more; for
your merchants were the great men of the earth, and all nations were deceived
by your sorcery. After this I heard what seemed to be the loud voice of a great
multitude in heaven, crying, "Hallelujah! Salvation and glory and power
belong to our God, for his judgments are true and just; he has judged the great
harlot who corrupted the earth with her fornication, and he has avenged on her
the blood of his servants." Once more they cried, "Hallelujah! The
smoke from her goes up for ever and ever." And the angel said to me,
"Write this: Blessed are those who are invited to the marriage supper of
the Lamb."
The word of the Lord
RESPONSORIAL PSALM
Ps 100:1-<IP_ADDRESS> (R. Revelation 19:9a)
R. Blessed are those who are invited to the marriage
supper of the Lamb.
Cry out with joy to
the LORD, all the earth. Serve the LORD with gladness. Come before him, singing
for joy.
R. Blessed are those who are invited to the marriage
supper of the Lamb.
Know that he, the
LORD, is God. He made us; we belong to him. We are his people, the sheep of his
flock.
R. Blessed are those who are invited to the marriage
supper of the Lamb.
Enter his gates with
thanksgiving and his courts with songs of praise. Give thanks to him, and bless
his name.
R. Blessed are those who are invited to the marriage
supper of the Lamb.
Indeed, how good is
the LORD, eternal his merciful love. He is faithful
from age to age.
R. Blessed are those who are invited to the marriage
supper of the Lamb.
ALLELUIA Luke 21:28
R. Alleluia. Look up and raise your heads, because your
redemption is drawing near. R. Alleluia.
GOSPEL
Luke 21:20-28
“Jerusalem will be trodden down by the Gentiles, until the times of the Gentiles are fulfilled.”
A reading from the holy Gospel according to Luke
At that time: Jesus
said to his disciples, "When you see Jerusalem
surrounded by armies, then know that its desolation
has come near. Then let those who are in Judea flee to the mountains, and let
those who are inside the city depart, and let not those who are out in the
country enter it; for these are days of vengeance, to fulfil all that is
written.  For great distress shall be upon the earth and wrath upon this people;
they will fall by the edge of the sword, and be led captive among all nations;
and Jerusalem
will be trodden down by the Gentiles, until the times of the Gentiles are
fulfilled. "And there will be signs in sun and moon and stars, and upon
the earth distress of nations in perplexity at the roaring of the sea and the
waves, men fainting with fear and with foreboding of what is coming on the
world; for the powers of the heavens will be shaken. And then they will see the
Son of man coming in a cloud with power and great glory. Now when these things
begin to take place, look up and raise your heads, because your redemption is
drawing near."
The Gospel of the Lord


Objects
- Vegetation (Dark Green)
- Earth (Dark Yellow)
- Water (Light Yellow with no vegetation, Light Green with vegetation)
- Organism (Varying colors - see Base Organism Stats)
Base Organism Stats
- Size (percentage). It is never below 1% size. Size is indicated by saturation.
- Aquatic (percentage) is indicated by hue (blue is more aquatic, red is more landlubber).
- Carnivorous (percentage) is indicated by luminosity. A higher level allows organisms to relieve more hunger from flesh.
- Sex (male/female) (pretty self explanitory).
- Pregnant. Only for female organisms.
- Age (number of turns) does not directly kill an animal, however, it will affect the variables such as Speed stat and Mature property (both explained below).
Mating
- An organism will only be Mature if its age is greater than 100 times Size. So an organism of Size 20% will only be considered Mature once it reaches an Age of 20 turns. Organisms will only breed once they are Mature.
- A female organism will be Pregnant after mating. The gestation period is proportional to the Size of the organism, plus 5 turns. An organism of Size 20% will take 20 turns to give birth.
- Two adjacent organisms have a 50% chance of mating per turn.
- When two organisms breed, the offspring will deviate by at most 1% of the extreme for each parent. For instance, if the parents have sizes of 5% and 8%, the child will have a uniform chance of having size 4% to 9%.
- Organisms will only breed with organisms that have a mean base stat difference of at most 2%. Thus, it is technicaly possible, though improbable, for an organism to mate with and eat another organism.
- Mating will last for 5 turns.

Variable Organism Stats
- Herbivorous (percentage) is indicated by blackness. A higher level allows organisms to relieve more hunger from vegetation. The sum of the Herbivorous stat and the Carnivorous stat will always sum to 100%.
- Speed (pertentage, between 1% and 100%). Based on Size and Aquatic stats, Age, and the extent of Pregnancy for female organisms. The larger the organism, the slower. Speed in water vs land is dependent on Aquatic stat.
- Sight (Manhattan distance between 1 and 10 tiles). Based on Size, Aquatic, and Carnivorous stats.
- Swimming (pertentage). Positive linearly correlation to the Aquatic stat. A higher Swimming stat indicates the chance of an organism leaving water. Hence, with a 100% Swimming stat, an organism will never leave the water. Note, however, that it will still be able to move on land if it is born on land. But once it enters water, it will never go to land again.
- Walking (pertentage). Negative linearly correlation to the Aquatic stat. Similar to Swimming, except
- Attack (pertentage). Based on Aquatic and Carnivorous stats. The more Carnivorous the organism, the higher the Attack. The Aquatic stat dictates the effectiveness of attack in water vs land.
- Defence (pertentage). Based on Aquatic and Size stats. The Aquatic stat dictates the effectiveness of defence in water vs land.
Eating
- Hunger. Increases linearly with Size. At birth, an organism starts with 0% Hunger. Every turn, the Hunger increases by 1% multiplied by the Size on the organism.
- If an organism has over 100% Hunger, it will die.
- It is impossible for an organism to less than -100% Hunger
- Eating vegetation decreases Hunger by the Herbivorous stat of the organism divided by its Size. Thus, completely herbivorous organisms with 100% Size must constantly be eating vegetation to survive forever. Additionally, organisms with Size less than 5% will reduce Hunger to 0% by eating a single tile of vegetation.
- Eating another organism decreases Hunger by twice times the Carnivorous level of the carnivorous organism. The Size of the other organism. So if an organism of Size 10% eats an organism of 5%, it will always return Hunger to 0%.
- The Carnivorous stat indicates how much Hunger is reduced by eating vegetation vs another organism.
- Aggression will determine how likely an organism is to eat another organism. The higher the Hunger is of an organism, the more likely it is to eat an organism with a higher Attack
Movement
- If a tile with edible contents is adjacent to an organism, it will try to move to that tile in proportion to how high its Hunger is. Thus, hungrier organisms are less likely to explore.
- Each organism has retains a Direction, which will change randomly with a 20% chance. The Direction is reset when an organism eats.
- With 100% Speed, and organism will move one tile every turn. With 1%, it will take an organism 100 turns to move a tile.
- Organisms are considered to occupy the tile that they mostly covering. So if an organism has move 25% onto a new tile, it is still considered on the starting tile. Once it is over 50% onto the next tile, it will be considered moved, and will consume whatever is on that tile. If an organism "accidentally" moves onto a tile with an organism that it can not eat, it will move back to the previous tile, reversing its Direction.
Vegetation
- Vegetation has a 1% chance of spreading to each adjacent tile each turn.


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Enugu: Pastor Paraded For Rape, Money Ritual
by Enioluwa AdeniyiThe founder of the Christ Mercy Ministry, Pastor Sunday Egbo, was on Tuesday paraded by the Enugu State Police Command for alleged rape and involvement in money rituals.
The commissioner of police in the state, Ahmed Abdur-Rahman while parading the cleric and 36 others at the command headquarters, noted that the Egbo had on November 9, 2019, invited a female member of his church under the pretext of organising a crusade starting from 6 pm.
Abdur-Rahman stated that the pastor asked the female church member to come along with the picture of her sick brother for prayers.
Abdur-Rahman stated, “On arrival, the pastor took her to his house and left while pretending to go and bring other people, who were supposed to participate in for the programme, but he returned around 11pm and dragged her into his room, brought out a magic stone and threatened to kill her if she raised the alarm.”
“He ordered her to remove her clothes, but the victim refused and the pastor slapped her and tore her clothes and raped her.”
“Egbo seized her pants, brassiere and brought out a small bottle and wanted to insert it in her private parts, but she struggled and ran naked to the door and shouted, which attracted neighbours, who invited the police to rescue her and arrest the pastor.”
Egbo admitted to the crime during an interview with newsmen, when asked why he was arrested by the police.
“I actually raped her in my house”.
The pastor noted that he approached a native doctor in Kogi State in his quest to be rich, he further revealed that the native doctor in question gave him a small coffin and other items and asked him to follow some instructions and he would become a millionaire.
He stated, “I paid the native doctor N200,000 to get the coffin and flute.
The state commissioner of police warned criminals in the state to repent or move out of the state as the command would stop at nothing to fish them out.
He said the 36 other suspects paraded committed various offences ranging from conspiracy, cultism, armed robbery, unlawful possession of firearms, murder and vandalism.
Abdur-Rahman said their arrests were made possible through intelligence-led policing measures in collaboration with local vigilante groups.


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