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AMISTAD A film review by Steve Rhodes Copyright 1997 Steve Rhodes
RATING (0 TO ****): ** 1/2
As the abolitionists kneel in prayer and song before the jail, the African slaves imprisoned inside cannot figure out what to make of this weird group. At first they think these strange people prostrating before them are sick, but then they decide that they must be entertainers. But why are the entertainers all so sad?
AMISTAD is Steven Spielberg's earnest movie based on a true story of a group of Africans who in 1839 revolted and commandeered the slave ship (La Amistad) upon which they were being transported. With the film, Spielberg boldly comes out foursquare against slavery -- not a very controversial or original idea.
Although Spielberg devises many powerful scenes for the movie, his staging confuses more often than it enlightens. When the slaves speak, for example, we frequently do not get English subtitles, but when their captors speak in Spanish, we get their every word translated.
Told by a host of known actors, almost all white, the overly long movie never takes the time to develop more than the one slave -- Joseph Cinque (Djimon Hounsou) -- into a real character. The other slaves make up a large faceless mass. With over two and a half hours of movie, in which Spielberg devotes time to incidents as small as young Queen Isabella of Spain (Anna Paquin) jumping up and down on her bed, why we couldn't hear from more than one slave remains a mystery.
In ROSEWOOD, a much better film from earlier this year, we heard from a wide variety of African-Americans, which made their plight real and vivid. Why did Spielberg feel he needed to devote so much money and screen time to a group of known actors (Anthony Hopkins, Matthew McConaughey, Morgan Freeman, Nigel Hawthorne, Stellan Skarsgard, Anna Paquin, David Paymer and Arliss Howard among others) as the people aiding the slaves and then create only a single decent part (Cinque) for the slaves themselves? How effective would SHINDLER'S LIST have been if only the Nazis and the Allies got parts, but the Jews were relegated to a single character?
After the slaves revolt in AMISTAD, they are tricked into sailing to America, thinking it is Africa. Once near the shore they were captured by an American naval ship and taken in for trial.
The case itself has many intricacies, both in the number of litigants and in the strategy of the slaves' defense attorney. Matthew McConaughey plays Roger Baldwin, the slaves' tricky but effective lawyer. Several groups sue for ownership of the slaves, but the script never takes the time to explore the issues as effectively as it should. Instead, we get a Cliff's Notes-style outline of the case plus a few random scenes of the trials.
When Spielberg should be storytelling, too often we get theatrics instead. Cinque, for example, bursts out in the middle of their trial and starts speaking for the first time in English. "Give us free," he shouts repeatedly, thereby bringing the trial to a complete standstill. Rather than anyone trying to repress the outbursts of the prisoner, everyone stares in awe at him as the music contains something approaching the sounds of the Mormon Tabernacle Choir raising a loud voice to heaven.
Morgan Freeman and Stellan Skarsgard play Joadson and Tappan, the two abolitionists who hire Baldwin. Freeman gets little dialog and is basically wasted in the movie. But then, most of the characters in the film exist mainly in caricature. Tappan, for example, is the canonical do-gooder who is perfectly willing to let others die for his cause. "They may be of more value to our cause in death than in life," Tappan explains to Baldwin.
Nigel Hawthorne repeats his Academy Award nominated role of George III, except this time his character is addressed as President Martin Van Buren.
The best performance in the picture, and the only one with any depth, is given by Anthony Hopkins as the semi-senile, ex-president John Quincy Adams. Adams comes into the story mainly at the last as he argues the slaves' case in front of the Supreme Court. "We've come to understand that who we are is who we were," he orates in his moving and complicated address to the court.
Even given the film's ponderous pacing, the slaves' story does come out. We see extremely gory pictures of naked people being beaten to death as their blood splatters everyone nearby, and we see naked human beings chained together, attached to a weight and thrown to their deaths in the ocean.
"Whoever tells the best story wins," is Adams's advice on how best to conduct the trial. Spielberg should have listened. All in all, AMISTAD is an excellent story, poorly told.
AMISTAD runs 2:37. It is rated R for gore, nudity and mature themes, and would be fine for teenagers who can handle gruesome images.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
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Broken URLs inthe reviews are the responsibility of the author.
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| 1.3.1.RELEASE | release-note | Getting started | 08/10/2021 |
| 1.3.0.RELEASE | release-note | Getting started | 01/09/2021 |
| 1.2.0.RELEASE | release-note | Getting started | 21/04/2021 |
| 1.1.0.RELEASE | release-note | Getting started | 30/10/2020 |
| 1.0.0.RELEASE | release-note | Getting started | 02/10/2020 |


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Company profile - Canadian Company Capabilities
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
G3 Consulting Ltd.
Mailing Address:
206-8501 162nd StSURREY, British Columbia
V4N 1B2
Location Address:
206-8501 162nd StSURREY, British Columbia
V4N 1B2
Tel.:<PHONE_NUMBER>
Fax:<PHONE_NUMBER>
Company Profile
Ownership Structure: Limited Company (closely held corporation)
Established: 1992 Burnaby, BC
Employees: 15-25
G3 Consulting Ltd. is a privately owned British Columbia company
with corporate offices located in Surrey, BC. G3 offers unique and
innovative environmental research and spatial data system
services to a wide variety of clients, combining quality science
and leading edge technology with proven expertise and
experience. G3 directs a team of scientific and technical
professionals on numerous projects from statistical design and
project management, through applied field and laboratory work,
data quality control and analysis,and comprehensive data
interpretation, mapping and reporting.
Primary Service Offering: G3 Consulting Ltd. is a service-first
based company providing innovative solutions in the fields of
environmental risk and aquatic effects evaluations, comprehensive
ecosystem assessments, forestry, mining, fish and fisheries
resource evaluations, watershed impacts and restoration
strategies, GIS, inventories mapping and environmental impacts
assessments, foreshore marine surveys, diver and ROV studies, oil and gas impact assessments, biophysical surveys, sediment and water studies, benthic/periphyton taxonomy and fisheries analysis.
Utilizing state-of-the-art assessment, data collection, mapping
and reporting technologies and highly experienced personnel, G3
ensures solutions are of high quality and usability, tailored to
the specific needs of its clients. G3 offers cost-effective
services world-wide, while ensuring a commitment to domestic
markets. Other services range from personnel training and
aquatic evaluations to environmental policy, data management and
systems design.
Key Technologies/Products/Services: Using proprietary
information systems and technologies, G3 Consulting Ltd. offers
a unique series of environmental evaluation and sampling
procedures, inventories, GIS, GPS and other mapping applications
and customized data management systems. These applications,
systems and technologies are designed to be flexible, handling a
wide variety of media and application needs, tailored to the
clients requirements and extensively tested for practicality and
reliability under rigorous test conditions.
Marketing Strategy: G3 Consulting Ltd. is committed to
scientifically defensible studies, designed and executed in a
cost-effective, efficient manner, ensuring that clients are well
represented and receive a product commensurate with intended
use. To this end, G3 maintains and develops relationships with
firms committed to these ideals and which offer complementary
services (engineering, laboratories, hydrology, etc.). G3 is
currently developing environmental, management system and
information exchange opportunities and expertise in the US and
South American regions. Inquires from like-minded firms wishing
to discuss strategic alliances or collaborations on joint-
venture opportunities are welcomed.
Key Clients: G3 Consulting Ltd. has developed a client base
which includes: federal, provincial and municipal governments
(Department of Fisheries and Oceans, industrial sector clients
such as Abitibi Consolidated, Fletcher Challenge, MacMillan
Bloedel Ltd., Harmac Pacific, Weldwood Canada, BC Rail, CN Rail,
CP Rail, Finlay Forest Industries, Riverside Forest Products,
Interfor, Vancouver International Airport, First Nations,
Stewardship groups, as well as additional national and
international companies and consulting firms.
Full profile
Full profile
Contact information
Company description
Product / Service / Licensing
services.
G3 Consulting Ltd. offers management services to government and
industry with experienced personnel using state-of-the-art
technology, responsive to the client's needs. G3 maintains
expertise in government regulations and policies, industrial
standards, management and practice codes, economic policies, and
environmental auditing requirements, enabling unique and cost-
effective environmental solutions. G3's experienced personnel
excel in designing environmental management solutions and create
flexible, responsive, multi-disciplinary teams and approaches
that surpass client expectations.
State-of-the art technologies applied to Project management,
statistical design and modelling, Environmental Effects
Monitoring, Watershed Restoration, water & wastewater
assessments, aquaculture umpacts, site assessments, commercial
dive services, benthic & periphyton taxonomy, agriculture
impacts, Fish and Fisheries enumerations, resource assessments,
EIA's, site audits, GIS mapping, Life Cycle Assesments,
contaminated sites assesments, property assessments, community
action plans, ultratrace contaminants assessments
(organochlorines, PCBs, etc.).
monitoring programs.
Environmental monitoring programs are designed and implemented
to define effects of industrial/municipal operations on the
receiving environment (e.g., pulpmill effluent discharge). These
programs encompass a broad range of disciplines from engineering
and statistics to fisheries and limnology. Effects monitoring
programs generally span a number of years and require the
management of numerous logistical details and personnel. G3 has
developed data management and handling criteria with rigorous
Quality Assurance/Quality Control (QA/QC) measures ensuring a
high level of data confidence.
Trend monitoring programs encompass the comprehensive monitoring
of industrial receiving environment constituents, both
temporally and spatially. Many of these programs are conducted
on behalf of the pulp and paper, forestry and mining industries,
and are designed to assess changes in environmental matrices as
a result of industrial process changes. G3 is focused on
appropriate study design integrity and program sensitivity which
are paramount in that program results are often disseminated to
public interest groups and the media. These programs often
require the analysis of a wide variety of environmental
components including sediments, receiving water, effluents, and
biological samples (vegetation, invertebrates, fish, benthos,
periphyton, and crab tissue, etc.). Ultratrace dissection,
handling and analyses of many of the samples collected
necessitated the development of exacting protocols for field,
laboratory, and data handling. G3 conducts ongoing marine and
terrestrial evaluations on behalf of government and industry.
G3 personnel are specialists in studies of this scope and work
with government and industry to develop scientifically-sound
programs which comply with or supersede environmental
regulations and policies. Areas of specialty include pulp and
paper, forestry, mining, petroleum and fisheries-based programs.
that develop custom software solutions for a variety of
industries. Environmental monitoring programs in numerous marine
and freshwater environments required the development of new and
sophisticated data management methodologies.
These programs required the accuracy and sensitivity to data
integrity of results from programs that required the analysis of
samples to ultratrace levels (parts per quadrillion).
As a supplement to G3 reporting systems, field and laboratory
data sheets are tailored to ensure that the appropriate
information is obtained and in the necessary format for entry
into a specific database. G3 provides custom database analysis
and programming services to the federal government and a variety
of industries.
B000-Chemical/Biological Studies and Analyses
B001-Endangered Species Studies - Plant and Animal
B002-Animal and Fisheries Studies
B004-Natural Resource Studies
B005-Oceanological Studies
B009-Other Natural Sciences Studies
B101-Environmental Studies Development of Environmental Impact Statements and Assessments
B102-Soil Studies
B103-Water Quality Studies
B104-Wildlife Studies
B109-Other Environmental Studies
E000-Environmental Services
E103-Water Quality Support Services
E104-Industrial Investigation Surveys and Technical Support Related to Water Pollution
E106-Toxic Substances Support Services
E108-Hazardous Substance Removal, Cleanup, and Disposal Services and Operational Support
E110-Industrial Investigations, Surveys and Technical Support for Multiple Pollutants
E199-Other Environmental Services
R000-Professional, Administrative and Management Support Services
R005-Technical Assistance
R006-Technical Writing Services
R014-Operations Research Studies / Quantitative Analysis Studies
R019-Other Professional Services
Technology profile
- Environmental Effects/Impact Assessments and Monitoring
- Fish, Fisheries Resources, Fish Habitats and Populations
Assessments
- Watershed Assessments and Restoration
- Riparian Ecology and Sediment Source Surveys
- Sediment, Toxicity and Benthos Evaluations
- Marine Evaluations
- First Nations Issues and Technical Training
- Contaminated Sites Assessments
- Ecological and Human Health Risk Assessments
- Spatial data management, modelling and mapping
- Hydrology and Groundwater Assessments
- Habitat Impact, Mitigation and Compensation Planning
- Toxicity and Contaminant-Specific Investigations
- Commercial/Biological Diving and Underwater Services
Market profile
Alliances:
- Financial
- Sales/Marketing
- Technology
Industry sector market interests:
- Environment
- Fishery
- Forestry
- Manufacturing
- Service Industry
Geographic markets:
Export experience:
- Chile
- India
- Japan
- Sweden
- United States
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Contacts
Contact information
Description
Company description
Products, services and licensing
Product / Service / Licensing
services.
G3 Consulting Ltd. offers management services to government and
industry with experienced personnel using state-of-the-art
technology, responsive to the client's needs. G3 maintains
expertise in government regulations and policies, industrial
standards, management and practice codes, economic policies, and
environmental auditing requirements, enabling unique and cost-
effective environmental solutions. G3's experienced personnel
excel in designing environmental management solutions and create
flexible, responsive, multi-disciplinary teams and approaches
that surpass client expectations.
State-of-the art technologies applied to Project management,
statistical design and modelling, Environmental Effects
Monitoring, Watershed Restoration, water & wastewater
assessments, aquaculture umpacts, site assessments, commercial
dive services, benthic & periphyton taxonomy, agriculture
impacts, Fish and Fisheries enumerations, resource assessments,
EIA's, site audits, GIS mapping, Life Cycle Assesments,
contaminated sites assesments, property assessments, community
action plans, ultratrace contaminants assessments
(organochlorines, PCBs, etc.).
monitoring programs.
Environmental monitoring programs are designed and implemented
to define effects of industrial/municipal operations on the
receiving environment (e.g., pulpmill effluent discharge). These
programs encompass a broad range of disciplines from engineering
and statistics to fisheries and limnology. Effects monitoring
programs generally span a number of years and require the
management of numerous logistical details and personnel. G3 has
developed data management and handling criteria with rigorous
Quality Assurance/Quality Control (QA/QC) measures ensuring a
high level of data confidence.
Trend monitoring programs encompass the comprehensive monitoring
of industrial receiving environment constituents, both
temporally and spatially. Many of these programs are conducted
on behalf of the pulp and paper, forestry and mining industries,
and are designed to assess changes in environmental matrices as
a result of industrial process changes. G3 is focused on
appropriate study design integrity and program sensitivity which
are paramount in that program results are often disseminated to
public interest groups and the media. These programs often
require the analysis of a wide variety of environmental
components including sediments, receiving water, effluents, and
biological samples (vegetation, invertebrates, fish, benthos,
periphyton, and crab tissue, etc.). Ultratrace dissection,
handling and analyses of many of the samples collected
necessitated the development of exacting protocols for field,
laboratory, and data handling. G3 conducts ongoing marine and
terrestrial evaluations on behalf of government and industry.
G3 personnel are specialists in studies of this scope and work
with government and industry to develop scientifically-sound
programs which comply with or supersede environmental
regulations and policies. Areas of specialty include pulp and
paper, forestry, mining, petroleum and fisheries-based programs.
that develop custom software solutions for a variety of
industries. Environmental monitoring programs in numerous marine
and freshwater environments required the development of new and
sophisticated data management methodologies.
These programs required the accuracy and sensitivity to data
integrity of results from programs that required the analysis of
samples to ultratrace levels (parts per quadrillion).
As a supplement to G3 reporting systems, field and laboratory
data sheets are tailored to ensure that the appropriate
information is obtained and in the necessary format for entry
into a specific database. G3 provides custom database analysis
and programming services to the federal government and a variety
of industries.
B000-Chemical/Biological Studies and Analyses
B001-Endangered Species Studies - Plant and Animal
B002-Animal and Fisheries Studies
B004-Natural Resource Studies
B005-Oceanological Studies
B009-Other Natural Sciences Studies
B101-Environmental Studies Development of Environmental Impact Statements and Assessments
B102-Soil Studies
B103-Water Quality Studies
B104-Wildlife Studies
B109-Other Environmental Studies
E000-Environmental Services
E103-Water Quality Support Services
E104-Industrial Investigation Surveys and Technical Support Related to Water Pollution
E106-Toxic Substances Support Services
E108-Hazardous Substance Removal, Cleanup, and Disposal Services and Operational Support
E110-Industrial Investigations, Surveys and Technical Support for Multiple Pollutants
E199-Other Environmental Services
R000-Professional, Administrative and Management Support Services
R005-Technical Assistance
R006-Technical Writing Services
R014-Operations Research Studies / Quantitative Analysis Studies
R019-Other Professional Services
Technology
Technology profile
- Environmental Effects/Impact Assessments and Monitoring
- Fish, Fisheries Resources, Fish Habitats and Populations
Assessments
- Watershed Assessments and Restoration
- Riparian Ecology and Sediment Source Surveys
- Sediment, Toxicity and Benthos Evaluations
- Marine Evaluations
- First Nations Issues and Technical Training
- Contaminated Sites Assessments
- Ecological and Human Health Risk Assessments
- Spatial data management, modelling and mapping
- Hydrology and Groundwater Assessments
- Habitat Impact, Mitigation and Compensation Planning
- Toxicity and Contaminant-Specific Investigations
- Commercial/Biological Diving and Underwater Services
Market
Market profile
Alliances:
- Financial
- Sales/Marketing
- Technology
Industry sector market interests:
- Environment
- Fishery
- Forestry
- Manufacturing
- Service Industry
Geographic markets:
Export experience:
- Chile
- India
- Japan
- Sweden
- United States


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`Star Trek: Insurrection' - A Familiar Frontier by Homer Yen (c) 1998
Something odd has happened to Commander Data (Brent Spiner). He was apart of an observation team that was sent to secretly observe a peaceful colony known as the Baku. But he exhibits insubordinate behavior by making his presence known to the villagers (a severe violation of a non-interference clause known to watchers of the series as the Prime Directive) and also attacks his fellow officers. What has caused Data to act this way? Captain Picard (Patrick Stewart) and the rest of the Enterprise crew, who have always entrusted their lives to Data, comes to his aid and subsequently launch an investigation to determine the motivation behind his actions. As a result, they uncover a secret plot that could forever affect the lives of the Baku. And although the Enterprise crew would be quick to defend the peaceful colonists against mighty oppressors, their actions may jeopardize a newfound alliance as well as their own future. It seems that an incredible phenomenon makes this planet something of a Fountain of Youth. Secrets learned from this planet may help humanity survive the ongoing challenges from hostile alien factions of the Star Trek universe. The wizened Picard, however, can not justify the forced removal of a people. He must find a way to protect them.
Unfortunately, Picard seemed like the only one that exhibited any kind of convictions. His Shakespearean background allowed him to inject a believable pathos into his role. The rest of the crew and even the tone of the movie, however, seemed to lack any kind of fervor, excitement or wit, which made the previous Star Trek movie (First Contact) so enjoyable. Much can be blamed on the subject material, which centers more on a moral battle rather than a life-or-death struggle. The goal of Picard's crusade didn't seem all that compelling, which made the whole movie experience somewhat uninteresting. One thing that I've always admired about many of the Star Trek storylines is that it frequently presents a plot that has some educational value - whether the episode is intended to impart a lesson or whether it reflects on a significant chapter of humanity's history. The drawback, however, is that the presentation can be somewhat sobering, and in a 2-hour, feature-length film, the message can wear thin rather quickly. So, as a fan of Star Trek, I enjoyed seeing the crew reassemble for another mission. But I was disappointed to a degree because this latest installment offered an all-to-familiar premise that amounted to a slightly better-than-average episode.
Grade: C+ if you're a Trek fan C if you're not.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


{% extends "otree/BaseSite.html" %} {% block title %} Persistent Lab URLs {% endblock %} {% block content %}
Here are some example persistent URLs you can use in the lab. They will stay constant for new sessions, even if the database is recreated.
{% for url in default_session_example_urls %}
{{ url }}
{% endfor %}
You should take these links, copy them for as many participants are in your session,
and modify the {{ participant_label }}
parameter of each link.
The above is just an example.
You can use whatever naming scheme you would like to use, e.g. {{ participant_label }}=PC-1
, {{ participant_label }}=PC-2
, etc.
Note the parameter {{ access_code_for_default_session }}
. This is defined in your settings.py
.
You can change it as frequently as you'd like, to prevent unauthorized server access.


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{% import 'webhooks/_activity/macros' as macros %}
{{ request.requestBody }}
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{{ 'No response yet.'|t('webhooks') }}


{% extends 'alerts/base.html' %} {% block content %}
Enter an event page and your email below for an alert when tickets become available again.


Hello. Below are the two different arrow configuration options.
Please use each and report back with the completed questions from the email.


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a survey of 16,819 banner-ads, from 42,300 websites and apps, over 365 days
There's a multitude of ad-spy tools out there: AdGooroo, SpyFu, AdBeat, WhatRunsWhere, MixRank, KeywordSpy, KeywordCompetitor, iSpionage, SEMRush, Follow.net, etc. I tried all of them. And they all suffer from the same downfall from my perspective: there's no differentiation between direct-response & branding ads.
So I set up my own system, (and you can too using 80legs.com or scrapinghub.com)
The collected data was scraped from 42,300 websites and apps, over 365 days. I juggled the data as part of my Exploratory Data Science course with Udacity.
Here are some examples of the 16,819 banners that were scraped, from 42,300 in 15 countries
Direct-Response, or Not Direct-Response?
 Big Brands have "burn budgets" and when they turn on 'the firehose', it is very easy to get misled into thinking a particular type of ad should be replicated. Or that there is something 'new' that now works in advertising, when in actual fact it doesn't perform.
So what are the criteria by which I decide whether or not an ad is "direct-response"?
This graph shows which banner ad size is more popular, per country.
468x60 is clearly the most prolific banner on the web
This Pie Chart shows the sampling sizes per country
| Country | banner-ads scraped |
|---|---|
| Australia | 1022 |
| Germany | 1226 |
| France | 1206 |
| Italy | 1159 |
| Netherlands | 950 |
| Turkey | 972 |
| UK | 1003 |
| US | 1229 |
| Spain | 1238 |
| Israel | 960 |
| Mexico | 1064 |
| Brazil | 1182 |
| Russia | 1142 |
| Sweden | 1278 |
Displays the quotient of population divided by amount of banner-ads found.


This project using Webpack for CSS and JS
Use project if need it, too. Without warranties, of course
All values, which you put on demo site will be deleted in 1 hour automatically (MySQL Events on the server)


Thus we conclude that sea ice interacts strongly with benthic blue carbon ecosystem services, partly through iceberg activity.
It is clear that sea ice and icebergs have complex influences on benthos and their blue carbon ecosystem services—and thus on the power of feedbacks on climate change. Pooling literature data sources [ 5, 6, 9, 14, 15, 19, 26, 27, 32] and iceberg-grounding hotspots in the current paper, we constructed a map of change in benthic blue carbon storage ecosystem services. Unsurprisingly, we found no data for even the majority of continental shelves around Antarctica, but we highlight likely blue carbon losses (blue cells, figure 5) in regions of frequent giant iceberg grounding. Fillinger et al. [ 19] have shown large blue carbon gains (albeit reported as colonization and growth of sponge increases) around collapsing ice shelves (dark red, figure 5). Thus, the recent declaration that such areas attain Marine Protected Area status is good news for conservation of strong blue carbon ecosystem services, especially as evidence suggests these will strengthen if climate forces sea-ice losses [ 6]. Benthic blue carbon storage has increased at varying levels (yellow–red, figure 5) around West Antarctic shelves, including parts of the Weddell and Ross Seas [ 5]. The recent international Antarctic Circumnavigation Expedition (ACE) enabled various samples to be taken at key locations (white cells with ‘?’, figure 5), which should reveal blue carbon trends following analysis. Finally, there are areas for which there are some samples but for which no trend is apparent (green, figure 5), though this may be from paucity of samples, analyses or unclear identification.
Trends in benthic blue carbon change. Change in blue carbon benthic standing stock (tonnes per square kilometre) around the Southern Ocean. Each cell is 3° × 3°. Cell data based on: (i) yellow–red are increases from direct (increased standing stock from sampling benthos) and indirect (iceberg grounding data) evidence streams, Fillinger et al. [ 19], Barnes [ 5, 9], Barnes & Sands [ 9]; (ii) white with question mark are samples awaiting analysis (from Antarctic Circumnavigation Expedition cruise 2016–2017) and expected samples from British Antarctic Survey JR17004 scientific cruise; (iii) light to dark blue are decreases from giant iceberg grounding probabilities given by US National Ice Center ( figure 4a).
Our calculations suggest that giant icebergs alone contribute a net gain of 10 6 tonnes of C immobilized per year (around the Antarctic Peninsula), which matches similarly sized gains in the same region driven by sea-ice losses [ 9]. The considerable interest and concern about icebergs, particularly giants, focuses on potential natural environment ‘damage’. However, our work supports recent studies (e.g. [ 19]), which show that iceberg effects are more complex and can aid ecosystem development and identification of negative feedbacks on climate change.
On the scale of terrestrial forests and oceanic water absorption, Antarctic benthos carbon cycling is small, at just 10 6 tonnes for increases from ice shelf losses (by our calculations) in addition to 10 7 tonnes for increases driven by sea-ice losses [ 7, 9]. Recently, Ashton et al. [ 37] demonstrated that in Antarctic coastal waters warming of just one degree increases growth by more than expected amounts. This gives reason to hypothesize that benthos on the warmer and vast Arctic and sub-Antarctic shelves (e.g. 2.2 × 10 6 km 2 of Kerguelen Plateau) may also be increasing blue carbon storage—and when quantified could prove the single largest negative feedback (on climate change). All three (Antarctic, sub-Antarctic and Arctic) of these sinks are expected to increase in response to climate change, in contrast to shrinking forests (and in the sea mangroves, seagrass beds and saltmarsh areas) and become progressively less efficient at ocean sponging [ 38]. Sub-Arctic forests do represent a negative feedback [ 39] but of smaller magnitude than polar and subpolar continental shelves. Cold water benthic blue carbon responses to (Arctic and West Antarctic) losses of sea ice, ice shelves and warming probably represent the three largest negative feedbacks yet among those which are most poorly characterized and understood. The current work at least attempts to evaluate the iceberg component of these.


Package - product_package
product_package is a package that contains procedures related to product management.
- create_product - Creates customers with verification of duplicate values
- verify_product_availability - Generates a list of customers who are eligible for the
company's promotion based on the birth date
- write_catalog - Returns a list of the customers in the form of the reference cursor
Demonstrated features
- Definitions:-
- CREATE OR REPLACE PACKAGE ... IS
- CREATE OR REPLACE PACKAGE BODY... IS
- CREATE OR REPLACE PROCEDURE ... IS
- CREATE OR REPLACE FUNCTION ... IS
- Cursor Support:-
- CURSOR ... IS SELECT ...
- OPEN, FETCH, CLOSE
- %FOUND, %NOTFOUND, %ISOPEN
- PL/SQL syntax:-
- := - assignment
- /* ... */ - block comments
- Built-in packages:-
- DBMS_OUPUT
- UTL_FILE
- UTL_DIR
- New SQL syntax:-
- ... START WITH ... CONNECT BY PRIOR ...
- LEVEL
- (+) - outer join
- New sequences keywords:-


Pokemon: Indigo League Series 2
Pokemon Ambulance is the second episode of
"Pokemon: Indigo League"
, the first season of the original series.


covidbility – noun [ C / U ]
A number that represents how likely one person gets infected by a novel coronavirus.
People that you get closer than 2 meters (6 feets) of distance for a significant time.
Simplified model, learn more in Notes.
You can move people between days.
-5
-%
-4
-%
-3
-%
-2
-%
Yest.
- %
Today
- %
All ages: %, age 0-9: %, 10-19 %, 20-29 %, 30-39 %, 40-49 %, 50-59 %, 60-69 %, 70-79 %, 80+ %
Load data for World (average), Spain, USA (derived)
All ages: %, age 0-9: %, 10-19 %, 20-29 %, 30-39 %, 40-49 %, 50-59 %, 60-69 %, 70-79 %, 80+ %
Load data for World (average), China, Italy, Netherlands, South Korea, Spain
0.2109 %
chance to get infected.0.0421 %
chance to need hospitalization or ICU.0.0071 %
chance that you will die.0.0182 %
.Notes:
– This is very simplified model for information purpose only.
– We do not intend to provide any medical advice.
– We are not epidemiologists or medical scientists.
– The model does not count on transmission risk, gender, wearing masks, passing people on the street or touching infected surfaces.
– We use data from pomber / covid19 and samayo / country-json. Different countries count cases and test populations differently.
– Chance of having COVID-19 = ((Conf.C. − Rec.) × multiplicator of Undoc.C. − Deaths) / Popul. × 100.
– Default hospitalization and fatality rates based on multiple sources, e.g. The Covid-19 risks for different age groups, explained, Coronavirus Disease (COVID-19) – Statistics and Research. The model does not count with number of hospital beds or comorbidity.
– Vehicle crash probability based on Global Status Report on Road Safety 2018.


Hey Come Check Out our Roman Numeral Generator!
Enter a year no higher than 3,999 and get it in Roman Numerals
The Magic Number
Here's your Roman Numerals:


API¶
Import epiScanpy’s high-level API as:
import episcanpy.api as epi
Count Matrices: CT¶
Loading data, loading annotations, building count matrices, filtering of lowly covered methylation variables. Filtering of lowly covered cells.
Building count matrices¶
Quickly build a count matrix from tsv/tbi file.
|
Building count matrix on the fly. |
Load features¶
In order to build a count matrix for either methylation or open chromatin data, loading the segmentation of the genome of interest or the set of features of interest is a prerequirement.
|
The function load features is here to transform a bed file into a usable set of units to measure methylation levels. |
|
Generate windows/bins of the given size for the appropriate genome (default choice is human). |
|
If the features loaded are too smalls or of different sizes, it is possible to normalise them to a unique given size by extending the feature coordinate in both directions. |
|
Plot the different feature sizes in an histogram. |
|
Extract the names of the loaded features, specifying the chromosome they originated from. |
Reading methylation file¶
Functions to read methylation files, extract methylation and buildthe count matrices:
|
Build methylation count matrix for a given annotation. |
|
Read file from which you want to extract the methylation level and (assuming it is like the Ecker/Methylpy format) extract the number of methylated read and the total number of read for the cytosines covered and in the right genomic context (CG or CH) :param sample_name: name of the file to read to extract key information. |
|
read the raw count matrix and convert it into an AnnData object. |
Reading open chromatin(ATAC) file¶
ATAC-seq specific functions to build count matrices and load data:
|
Build a count matrix one set of features at a time. |
|
Convert regular atac matrix into a sparse Anndata: |
General functions¶
Functions non -omic specific:
|
Convert regular atac matrix into a sparse Anndata: |
Preprocessing: PP¶
Imputing missing data (methylation), filtering lowly covered cells or variables, correction for batch effect.
|
Histogram of the number of open features (in the case of ATAC-seq data) per cell. |
|
Correlation between a given PC and a covariate. |
|
Display how often a feature is measured as open (for ATAC-seq). |
|
Display how often a feature is measured as open (for ATAC-seq). |
|
This function computes a variability score to rank the most variable features across all cells. |
|
Show distribution plots of cells sharing features and variability score. |
|
This function computes a variability score to rank the most variable features across all cells. |
|
convert the count matrix into a binary matrix. |
|
Automatically computes PCA coordinates, loadings and variance decomposition, a neighborhood graph of observations, t-distributed stochastic neighborhood embedding (tSNE) Uniform Manifold Approximation and Projection (UMAP) |
|
Load observational metadata in adata.obs. |
|
Load sparse matrix (including matrices corresponding to 10x data) as AnnData objects. |
|
Filter cell outliers based on counts and numbers of genes expressed. |
|
Filter features based on number of cells or counts. |
|
Normalize counts per cell. |
|
Principal component analysis [Pedregosa11]. |
|
Normalize total counts per cell. |
|
Regress out unwanted sources of variation. |
|
Subsample to a fraction of the number of observations. |
|
Downsample counts from count matrix. |
|
Compute a neighborhood graph of observations [McInnes18]. |
|
Transform adata.X from a matrix or array to a sparse matrix. |
|
Logarithmize the data matrix. |
|
Transform adata.X from a matrix or array to a sparse matrix. |
Methylation matrices¶
Methylation specific count matrices.
|
Impute missing values in methyaltion level matrices. |
|
read the raw count matrix and convert it into an AnnData object. |
|
Temporary function to load and impute methyaltion count matrix into an AnnData object |
Tools: TL¶
|
It is a wrap-up function of scanpy sc.tl.rank_genes_groups function. |
|
Automatically computes PCA coordinates, loadings and variance decomposition, a neighborhood graph of observations, t-distributed stochastic neighborhood embedding (tSNE) Uniform Manifold Approximation and Projection (UMAP) |
|
Convert list of known cell type markers from literature to a dictionary Input list of known marker genes First row is considered the header |
|
Use markers of a given cell type to plot peak openness for peaks in promoters of the given markers Input cell type, cell type markers, peak promoter intersections |
|
Deprecated - Please use epi.tl.var_features_to_genes instead. |
|
Once you called the most variable features. |
|
Build an AnnData object containing the number of open features (windows, peaks, etc) overlapping genes (gene bodies + 5kb upstream of the TSS). |
|
Diffusion Maps [Coifman05] [Haghverdi15] [Wolf18]. |
|
Force-directed graph drawing [Islam11] [Jacomy14] [Chippada18]. |
|
t-SNE [Maaten08] [Amir13] [Pedregosa11]. |
|
Embed the neighborhood graph using UMAP [McInnes18]. |
|
Infer progression of cells through geodesic distance along the graph [Haghverdi16] [Wolf19]. |
|
Cluster cells into subgroups [Blondel08] [Levine15] [Traag17]. |
|
Cluster cells into subgroups [Traag18]. |
|
Compute kmeans clustering using X_pca fits. |
|
Compute hierarchical clustering using X_pca fits. |
|
Function will test different settings of louvain to obtain the target number of clusters. |
|
Computes a hierarchical clustering for the given groupby categories. |
|
Compute Adjusted Rand Index. |
|
Compute adjusted Mutual Info. |
|
Compute homogeneity score. |
|
Compute silhouette scores. |
Plotting: PL¶
The plotting module episcanpy.plotting
largely parallels the tl.*
and a few of the pp.*
functions.
For most tools and for some preprocessing functions, you’ll find a plotting function with the same name.
|
Scatter plot in PCA coordinates. |
|
Plot PCA results. |
|
Plot the variance ratio. |
|
Scatter plot in tSNE basis. |
|
Scatter plot in UMAP basis. |
|
Plot ranking of features. |
|
Plot ranking of features for all tested comparisons. |
|
Plot ranking of features using dotplot plot (see |
|
Plot ranking of features using stacked_violin plot (see |
|
Plot ranking of features using matrixplot plot (see |
|
Plot ranking of features using heatmap plot (see |
|
Plot ranking of features using heatmap plot (see |
|
Show distribution plots of cells sharing features and variability score. |
|
Violin plot. |
|
Scatter plot along observations or variables axes. |
|
Plot rankings. |
|
Hierarchically-clustered heatmap. |
|
Stacked violin plots. |
|
Heatmap of the expression values of genes. |
|
Makes a dot plot of the expression values of var_names. |
|
Creates a heatmap of the mean expression values per group of each var_names. |
|
In this type of plot each var_name is plotted as a filled line plot where the y values correspond to the var_name values and x is each of the cells. |
|
Plots a dendrogram of the categories defined in groupby. |
|
Plots the correlation matrix computed as part of sc.tl.dendrogram. |
|
% or cell count corresponding to the overlap of different cell types between 2 set of annotations/clusters. |
|
Heatmap of the cluster correspondance between 2 set of annaotations. |
|
|
|
Plot the product of tl.silhouette as a silhouette plot |
|
Both compute silhouette scores and plot it. |
|
Show distribution plots of cells sharing features and variability score. |
|
This function computes a variability score to rank the most variable features across all cells. |


Chest of Bifrons:
A heavy wooden chest, soaked with sea water. Some strange, hieroglyphic characters are engraved around the lock.
Quest


{{ with .Site.Params.Description }}{{ . }}{{ end }}
Welcome!
We are a para-church organization that is holding fast to the truth of the gospel, strengthening students in their faith, and empowering students to proclaim a message of hope to those around them.
Events
Guys Night
Every Thursday
100 N University Dr, Edmond, OK 73034
We are bringing the men together for a night of games, fun, Jesus, and brotherhood.
Navigate
Every Thursday
100 N University Dr, Edmond, OK 73034
This is a time to dig deeper in the word and in our faith.
Girls Night
Every Thursday
100 N University Dr, Edmond, OK 73034
We are bringing the women together for a night of games, fun, Jesus, and womenhood.
Girls Night
Every Thursday
100 N University Dr, Edmond, OK 73034
We are bringing the women together for a night of games, fun, Jesus, and womenhood.
Girls Night
Every Thursday
100 N University Dr, Edmond, OK 73034
We are bringing the women together for a night of games, fun, Jesus, and womenhood.


Password Storage, Management, and Sharing
<EMAIL_ADDRESS>Tools > Preferences (Ctrl + O)
"Save my password during login/registration" – turn off
"Ask to save form-fill data that I type into web forms" – turn off
Marked as #1 on following screenshot."Save passwords with auto-login option by default" – turn off
"Always require Master Password" – within password editor – turn on
"Automatic logout" – under Security – unlock settings, turn on, set timeframe
Marked as #2 on previous and following screenshots.Master Password Cannot Be Accessed
Company Administrators Cannot See Passwords
Nothing in Personal Space is Monitored
Any time you share a password or access a company password.
This Information: https://kb.khmtravel.com/dashlane/
Tutorial Videos: http://bit.ly/khm-dashlane-videos/
One-on-One Assistance: systems@khmtravel.com


We’re delighted to announce that Bitpanda has decided to list us on their exchange. This means you can buy $KMD with fiat, sepa and Mastercard. It’s good news. Please share. This means you have a new alternative to coinbase and $BTC transfers to a $KMD listed exchange! It...


Learning Puppet — Basic Agent/Master Puppet
This guide assumes that you’ve followed the previous walkthrough, and have a fresh agent VM that can reach your original master VM over the network. Both VMs should be running right now, and you’ll need to be logged in to both of them as root.
Introduction
How Do Agents Get Configurations?
Puppet’s agent/master mode is pull-based. Usually, agents are configured to periodically fetch a catalog and apply it, and the master controls what goes into that catalog. (For the next few exercises, though, you’ll be triggering runs manually.)
What Do Agents Do, and What Do Masters Do?
Earlier, you saw this diagram of how Puppet compiles and applies a manifest:
Running Puppet in agent/master mode works much the same way — the main difference is that it moves the manifests and compilation to the puppet master server. Agents don’t have to see any manifest files at all, and have no access to configuration information that isn’t in their own catalog.
The Agent Subcommand
The puppet agent subcommand fetches configurations from a master server. It has two main modes:
- Daemonize and fetch configurations every half-hour (default)
- Run once and quit
We’ll be using the second mode, since it gives a better view of what’s going on. To keep the agent from daemonizing, you should use the --test
option, which also prints detailed descriptions of what the agent is doing.
If you accidentally run the agent without --test
, it will daemonize and run in the background. To check whether the agent is running in the background, run:
# /etc/init.d/pe-puppet status
To turn it off, run:
# /etc/init.d/pe-puppet stop
Saying Hi
Time to start! On your agent VM, start puppet agent for the first time:
[root@agent1 ~]# puppet agent --test
info: Creating a new SSL key for agent1.localdomain
warning: peer certificate won't be verified in this SSL session
info: Caching certificate for ca
warning: peer certificate won't be verified in this SSL session
warning: peer certificate won't be verified in this SSL session
info: Creating a new SSL certificate request for agent1.localdomain
info: Certificate Request fingerprint (md5): FD:E7:41:C9:2C:B7:5C:27:11:0C:8F:9C:1D:F6:F9:46
warning: peer certificate won't be verified in this SSL session
warning: peer certificate won't be verified in this SSL session
warning: peer certificate won't be verified in this SSL session
Exiting; no certificate found and waitforcert is disabled
Hmm.
What Happened?
Puppet agent found the puppet master, but it got stopped at the certificate roadblock. It isn’t authorized to fetch configurations, so the master is turning it away.
Troubleshooting
It’s possible you didn’t see the response printed above, and there are a number of possible culprits. Read back over the instructions for creating your agent VM and make sure you didn’t miss anything; in particular, check that:
- The VMs can ping each other
- The agent can resolve the puppet master by host name
- The agent’s
/etc/puppetlabs/puppet/puppet.conf
file has aserver
setting (in the[agent]
block) ofpuppet
orlearn.localdomain
- The VMs’ clocks are in sync
Signing the Certificate
So we’ll authorize it! On your puppet master VM, check the list of outstanding certificate requests with puppet cert list
. (More about this command later.)
[root@learn ~]# puppet cert list
agent1.localdomain (FD:E7:41:C9:2C:B7:5C:27:11:0C:8F:9C:1D:F6:F9:46)
There’s our agent node. And the request fingerprint matches, too. You know this node is okay, so go ahead and sign its certificate with puppet cert sign
:
[root@learn ~]# puppet cert sign agent1.localdomain
notice: Signed certificate request for agent1.localdomain
notice: Removing file Puppet::SSL::CertificateRequest agent1.localdomain at '/etc/puppetlabs/puppet/ssl/ca/requests/agent1.localdomain.pem'
Now that it’s authorized, go back to the agent VM and run puppet agent again:
[root@agent1 ~]# puppet agent --test
warning: peer certificate won't be verified in this SSL session
info: Caching certificate for agent1.localdomain
info: Retrieving plugin
info: Caching certificate_revocation_list for ca
info: Loading facts in facter_dot_d
info: Loading facts in facter_dot_d
info: Loading facts in facter_dot_d
info: Loading facts in facter_dot_d
info: Caching catalog for agent1.localdomain
info: Applying configuration version '1326210629'
notice: Finished catalog run in 0.11 seconds
It worked! That was a successful Puppet run, although it didn’t do much yet.
What Happened?
Puppet uses SSL certificates to protect communications between agents and the master. Since agents can’t do a full run without a certificate, our agent had to ask for one and then wait for the request to get approved.
We’ll cover SSL in more detail later.
Serving a Real Configuration
So how can we make the agent do something interesting? Well, we already built some useful classes, and they’re all available on the puppet master, so we’ll use them. (If you haven’t already copied the modules from your old VM into your puppet master’s /etc/puppetlabs/puppet/modules
directory, do so now.)
But how do we choose which classes go into an agent’s catalog?
Site.pp
When we were using puppet apply, we would usually specify a manifest file, which declared all of the classes or resources we wanted to apply.
The puppet master works the same way, except that it always loads the same manifest file, which we usually refer to as site.pp. With Puppet Enterprise, it’s located by default at /etc/puppetlabs/puppet/manifests/site.pp
, but you can configure its location with the manifest
setting.
You could declare classes and resources directly in site.pp, but that would make every node get the same resources in its catalog, which is of limited use. Instead, we’ll hide the classes we want to declare in a node definition.
Node Definitions
Node definitions work almost exactly like class definitions:
# Append this at the bottom of /etc/puppetlabs/puppet/manifests/site.pp
node 'agent1.localdomain' {
# Note the quotes around the name! Node names can have characters that
# aren't legal for class names, so you can't always use bare, unquoted
# strings like we do with classes.
# Any resource or class declaration can go inside here. For now:
include apache
class {'ntp':
enable => false,
ensure => stopped,
}
}
But unlike classes, nodes are declared automatically, based on the name of the node whose catalog is being compiled. Only one node definition will get added to a given catalog, and any other node definitions are effectively hidden.
An agent node’s name is almost always read from its certname
setting, which is set at install time but can be changed later. The certname is usually (but not always) the node’s fully qualified domain name.
More on node definitions later, as well as alternate ways to assign classes to a node.
Pulling the New Configuration
Now that you’ve saved site.pp with a node definition that matches the agent VM’s name, go back to that VM and run puppet agent again:
[root@agent1 ~]# puppet agent --test
info: Retrieving plugin
info: Loading facts in facter_dot_d
info: Loading facts in facter_dot_d
info: Loading facts in facter_dot_d
info: Loading facts in facter_dot_d
info: Caching catalog for agent1.localdomain
info: Applying configuration version '1326416535'
notice: /Stage[main]/Ntp/Package[ntp]/ensure: created
--- /etc/ntp.conf 2011-11-18 13:21:25.000000000 +0000
+++ /tmp/puppet-file20120113-5967-56l9xy-0 2012-01-13 01:02:23.000000000 +0000
@@ -14,6 +14,8 @@
# Use public servers from the pool.ntp.org project.
# Please consider joining the pool (http://www.pool.ntp.org/join.html).
+
+# Managed by puppet class { "ntp": servers => [ ... ] }
server 0.centos.pool.ntp.org
server 1.centos.pool.ntp.org
server 2.centos.pool.ntp.org
info: /Stage[main]/Ntp/File[ntp.conf]: Filebucketed /etc/ntp.conf to main with sum 5baec8bdbf90f877a05f88ba99e63685
notice: /Stage[main]/Ntp/File[ntp.conf]/content: content changed '{md5}5baec8bdbf90f877a05f88ba99e63685' to '{md5}35ea00fd40740faf3fd6d1708db6ad65'
notice: /Stage[main]/Apache/Package[apache]/ensure: created
notice: /Stage[main]/Apache/Service[apache]/ensure: ensure changed 'stopped' to 'running'
info: ntp.conf: Scheduling refresh of Service[ntp]
notice: /Stage[main]/Ntp/Service[ntp]: Triggered 'refresh' from 1 events
notice: Finished catalog run in 32.74 seconds
Success! We’ve pulled a configuration that actually does something.
If you change this node’s definition in site.pp, it will fetch that new configuration on its next run (which, in a normal environment, would happen less than 30 minutes after you make the change).
More Installments Coming Later
You now know how to:
- Run puppet agent interactively with
--test
- Authorize a new agent node to pull configurations from the puppet master
- Use node definitions in site.pp to choose which classes go into a given node’s catalog
But there are some important details we’ve glossed over. In a future installment, we’ll talk more about certificates and node classification.


User stories:
- As an unauthenticated user, I can login with Twitter.
- As an authenticated user, I can link to images.
- As an authenticated user, I can delete images that I've linked to.
- As an authenticated user, I can see a Pinterest-style wall of all the images I've linked to.
- As an unauthenticated user, I can browse other users' walls of images.
- As an authenticated user, if I upload an image that is broken, it will be replaced by a placeholder image. (can use jQuery broken image detection)
- Hint: Masonry.js is a library that allows for Pinterest-style image grids.
Created by Ronald Blanco using clementine.js.


# Install and load packages ---------------
packages <- c(
"tidyverse",
"ggrepel",
"WDI",
"forcats",
"grid",
"gridExtra",
"countrycode",
"lmtest",
"sandwich",
"scales",
"haven",
"kableExtra",
"magrittr",
"rstan"
)
# Change to install = TRUE to install the required packages
pacman::p_load(packages, character.only = TRUE)
The stan files used in this document are almost entirely based on the codes written by Lee, Feller, and Rabe-Hesketh. Their original code is found here. Their documentation on this code is found here.
df <- read_dta("http://www.nber.org/~rdehejia/data/nsw_dw.dta") %>%
mutate(
earn74 = re74 / 1000,
earn75 = re75 / 1000,
earn78 = re78 / 1000,
earn74_0 = (re74 == 0),
earn75_0 = (re75 == 0),
earn78_0 = (re78 == 0),
)
cbind(
c(
"age", "education", "married", "nodegree", "black",
"earn'74", "earn'74 = 0", "earn'75", "earn'75 = 0", "earn'78", "earn'78 = 0"
),
df %>%
summarise_at(
vars(age, education, married, nodegree, black, earn74, earn74_0, earn75, earn75_0, earn78, earn78_0),
list(mean, sd)
) %>%
pivot_longer(cols = everything(), names_to = c(".value", "fn"), names_patter = "(.*)(_.*$)") %>%
mutate(
across(-fn, ~ formatC(., digits = 2, format = "f")),
across(-fn, ~ ifelse(fn == "_fn2", paste0("(", ., ")"), .)),
) %>%
select(-fn) %>%
t(),
df %>%
filter(treat == 0) %>%
summarise_at(
vars(age, education, married, nodegree, black, earn74, earn74_0, earn75, earn75_0, earn78, earn78_0),
mean
) %>%
mutate(
across(everything(), ~ formatC(., digits = 2, format = "f")),
) %>%
t(),
df %>%
filter(treat == 1) %>%
summarise_at(
vars(age, education, married, nodegree, black, earn74, earn74_0, earn75, earn75_0, earn78, earn78_0),
mean
) %>%
mutate(
across(everything(), ~ formatC(., digits = 2, format = "f")),
) %>%
t()
) %>%
set_rownames(NULL) %>%
kable("html", booktabs = TRUE, align = c("l", rep("c", 4))) %>%
add_header_above(
c(" ", " ", " ", "($N_c$ = 260)", "($N_t$ = 185)")
) %>%
add_header_above(
c("Covariate", "Mean", "(S.D.)", "Average Controls", "Average Treated")
) %>%
kable_styling(position = "center")
|
Covariate
|
Mean
|
(S.D.)
|
Average Controls
|
Average Treated
|
|
|
|
|
(\(N_c\) = 260)
|
(\(N_t\) = 185)
|
|
age
|
25.37
|
(7.10)
|
25.05
|
25.82
|
|
education
|
10.20
|
(1.79)
|
10.09
|
10.35
|
|
married
|
0.17
|
(0.37)
|
0.15
|
0.19
|
|
nodegree
|
0.78
|
(0.41)
|
0.83
|
0.71
|
|
black
|
0.83
|
(0.37)
|
0.83
|
0.84
|
|
earn’74
|
2.10
|
(5.36)
|
2.11
|
2.10
|
|
earn’74 = 0
|
0.73
|
(0.44)
|
0.75
|
0.71
|
|
earn’75
|
1.38
|
(3.15)
|
1.27
|
1.53
|
|
earn’75 = 0
|
0.65
|
(0.48)
|
0.68
|
0.60
|
|
earn’78
|
5.30
|
(6.63)
|
4.55
|
6.35
|
|
earn’78 = 0
|
0.31
|
(0.46)
|
0.35
|
0.24
|
df %>%
filter(treat == 0) %>%
ggplot(aes(x = earn78)) +
stat_bin(aes(y = ..count../sum(..count..)), binwidth = 1, boundary = 0) +
xlim(c(0, 80)) +
ylim(c(0, 0.5)) +
theme_minimal()
df %>%
filter(treat == 1) %>%
ggplot(aes(x = earn78)) +
stat_bin(aes(y = ..count../sum(..count..)), binwidth = 1, boundary = 0) +
xlim(c(0, 80)) +
ylim(c(0, 0.5)) +
theme_minimal()
Model 1
stan_data <- list(N = nrow(df), y = df$earn78, w = df$treat)
# Compile and run the stan model
fit_mod1 <- stan(
file = "stan_files/Ch8_Model_01.stan",
data = stan_data,
iter = 1000, chains = 4
)
Model 2
stan_data <- list(N = nrow(df), y = df$earn78, w = df$treat)
# Compile and run the stan model
fit_mod2 <- stan(
file = "stan_files/Ch8_Model_02.stan",
data = stan_data,
iter = 1000, chains = 4
)
## When you compile models, you are also contributing to development of the NEXT
## Stan compiler. In this version of rstan, we compile your model as usual, but
## also test our new compiler on your syntactically correct model. In this case,
## the new compiler did not work like we hoped. By filing an issue at
## https://github.com/stan-dev/stanc3/issues with your model
## or a minimal example that shows this warning you will be contributing
## valuable information to Stan and ensuring your models continue working. Thank you!
## This message can be avoided by wrapping your function call inside suppressMessages()
## or by first calling rstan_options(javascript = FALSE).
## Semantic error in 'string', line 5, column 7 to column 15:
## Identifier 'quantile' clashes with Stan Math library function.
##
## SAMPLING FOR MODEL 'Ch8_Model_02' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 7.1e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.71 seconds.
## Chain 1: Adjust your expectations accordingly!
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## Chain 1:
##
## SAMPLING FOR MODEL 'Ch8_Model_02' NOW (CHAIN 2).
## Chain 2:
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## Chain 2:
##
## SAMPLING FOR MODEL 'Ch8_Model_02' NOW (CHAIN 3).
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## Chain 3:
##
## SAMPLING FOR MODEL 'Ch8_Model_02' NOW (CHAIN 4).
## Chain 4:
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## Chain 4:
Model 3
# Add nine mean-centered covariates and their interaction terms
x <- as.matrix(df %>% select(age, education, married, nodegree, black, earn74, earn74_0, earn75, earn75_0))
x_mean_mat <- matrix(rep(apply(x, 2, mean, na.rm = TRUE), each = nrow(x)), nrow = nrow(x))
x_c_mat <- x - x_mean_mat
xw_inter <- x_c_mat * df$treat
colnames(xw_inter) <- paste0(colnames(x), "_w")
# Collect data into a list format suitable for Stan
stan_data <- list(
N = nrow(df),
N_cov = ncol(x_c_mat),
y = df$earn78,
w = df$treat,
x = x_c_mat,
xw_inter = xw_inter
)
# Compile and run the stan model
fit_mod3 <- stan(
file = "stan_files/Ch8_Model_03.stan",
data = stan_data,
iter = 1000, chains = 4
)
## When you compile models, you are also contributing to development of the NEXT
## Stan compiler. In this version of rstan, we compile your model as usual, but
## also test our new compiler on your syntactically correct model. In this case,
## the new compiler did not work like we hoped. By filing an issue at
## https://github.com/stan-dev/stanc3/issues with your model
## or a minimal example that shows this warning you will be contributing
## valuable information to Stan and ensuring your models continue working. Thank you!
## This message can be avoided by wrapping your function call inside suppressMessages()
## or by first calling rstan_options(javascript = FALSE).
## Semantic error in 'string', line 5, column 7 to column 15:
## Identifier 'quantile' clashes with Stan Math library function.
##
## SAMPLING FOR MODEL 'Ch8_Model_03' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 0.000156 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.56 seconds.
## Chain 1: Adjust your expectations accordingly!
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## Chain 1:
##
## SAMPLING FOR MODEL 'Ch8_Model_03' NOW (CHAIN 2).
## Chain 2:
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## Chain 2:
##
## SAMPLING FOR MODEL 'Ch8_Model_03' NOW (CHAIN 3).
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##
## SAMPLING FOR MODEL 'Ch8_Model_03' NOW (CHAIN 4).
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## Chain 4:
Model 4
# Add nine mean-centered covariates and their interaction terms
x <- as.matrix(
df %>%
select(age, education, married, nodegree, black, earn74, earn74_0, earn75, earn75_0) %>%
mutate(
earn74_0 = (earn74 > 0),
earn75_0 = (earn75 > 0),
)
)
x_mean_mat <- matrix(rep(apply(x, 2, mean, na.rm = TRUE), each = nrow(x)), nrow = nrow(x))
x_c_mat <- x - x_mean_mat
xw_inter <- x_c_mat * df$treat
colnames(xw_inter) <- paste0(colnames(x), "_w")
# Collect data into a list format suitable for Stan
stan_data <- list(
N = nrow(df),
N_cov = ncol(x_c_mat),
y = df$earn78,
y_pos = as.numeric(df$earn78 > 0),
z = df$treat,
x = x_c_mat,
xz_inter = xw_inter,
# x = x,
# xz_inter = x * df$treat,
rho = 0
)
# Compile and run the stan model
fit_mod4 <- stan(
file = "stan_files/Ch8_Model_04.stan",
data = stan_data,
iter = 1000, chains = 4
)
## When you compile models, you are also contributing to development of the NEXT
## Stan compiler. In this version of rstan, we compile your model as usual, but
## also test our new compiler on your syntactically correct model. In this case,
## the new compiler did not work like we hoped. By filing an issue at
## https://github.com/stan-dev/stanc3/issues with your model
## or a minimal example that shows this warning you will be contributing
## valuable information to Stan and ensuring your models continue working. Thank you!
## This message can be avoided by wrapping your function call inside suppressMessages()
## or by first calling rstan_options(javascript = FALSE).
## Semantic error in 'string', line 2, column 7 to column 15:
## Identifier 'quantile' clashes with Stan Math library function.
##
## SAMPLING FOR MODEL 'Ch8_Model_04' NOW (CHAIN 1).
## Chain 1: Rejecting initial value:
## Chain 1: Error evaluating the log probability at the initial value.
## Chain 1: Exception: lognormal_lpdf: Scale parameter is -1.90969, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 1: Rejecting initial value:
## Chain 1: Error evaluating the log probability at the initial value.
## Chain 1: Exception: lognormal_lpdf: Scale parameter is -0.494532, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 1: Rejecting initial value:
## Chain 1: Error evaluating the log probability at the initial value.
## Chain 1: Exception: lognormal_lpdf: Scale parameter is -0.72984, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 1: Rejecting initial value:
## Chain 1: Error evaluating the log probability at the initial value.
## Chain 1: Exception: lognormal_lpdf: Scale parameter is -0.360274, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 1: Rejecting initial value:
## Chain 1: Error evaluating the log probability at the initial value.
## Chain 1: Exception: lognormal_lpdf: Scale parameter is -0.26098, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 1: Rejecting initial value:
## Chain 1: Error evaluating the log probability at the initial value.
## Chain 1: Exception: lognormal_lpdf: Scale parameter is -0.678968, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 1: Rejecting initial value:
## Chain 1: Error evaluating the log probability at the initial value.
## Chain 1: Exception: lognormal_lpdf: Scale parameter is -0.877445, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 1: Rejecting initial value:
## Chain 1: Error evaluating the log probability at the initial value.
## Chain 1: Exception: lognormal_lpdf: Scale parameter is -1.93821, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 1: Rejecting initial value:
## Chain 1: Error evaluating the log probability at the initial value.
## Chain 1: Exception: lognormal_lpdf: Scale parameter is -0.00674907, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 1: Rejecting initial value:
## Chain 1: Error evaluating the log probability at the initial value.
## Chain 1: Exception: lognormal_lpdf: Scale parameter is -1.00088, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 1:
## Chain 1: Gradient evaluation took 0.000309 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.09 seconds.
## Chain 1: Adjust your expectations accordingly!
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##
## SAMPLING FOR MODEL 'Ch8_Model_04' NOW (CHAIN 2).
## Chain 2: Rejecting initial value:
## Chain 2: Error evaluating the log probability at the initial value.
## Chain 2: Exception: lognormal_lpdf: Scale parameter is -0.0542545, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 2: Rejecting initial value:
## Chain 2: Error evaluating the log probability at the initial value.
## Chain 2: Exception: lognormal_lpdf: Scale parameter is -0.269141, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 2: Rejecting initial value:
## Chain 2: Error evaluating the log probability at the initial value.
## Chain 2: Exception: lognormal_lpdf: Scale parameter is -1.19248, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 2: Rejecting initial value:
## Chain 2: Error evaluating the log probability at the initial value.
## Chain 2: Exception: lognormal_lpdf: Scale parameter is -1.18821, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 2: Rejecting initial value:
## Chain 2: Error evaluating the log probability at the initial value.
## Chain 2: Exception: lognormal_lpdf: Scale parameter is -1.32215, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 2:
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## Chain 3:
##
## SAMPLING FOR MODEL 'Ch8_Model_04' NOW (CHAIN 4).
## Chain 4: Rejecting initial value:
## Chain 4: Error evaluating the log probability at the initial value.
## Chain 4: Exception: lognormal_lpdf: Scale parameter is -1.52753, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 4: Rejecting initial value:
## Chain 4: Error evaluating the log probability at the initial value.
## Chain 4: Exception: lognormal_lpdf: Scale parameter is -0.500182, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 4: Rejecting initial value:
## Chain 4: Error evaluating the log probability at the initial value.
## Chain 4: Exception: lognormal_lpdf: Scale parameter is -0.0603489, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
## Chain 4: Rejecting initial value:
## Chain 4: Error evaluating the log probability at the initial value.
## Chain 4: Exception: lognormal_lpdf: Scale parameter is -1.84628, but must be > 0! (in 'modelc8e2427df18_Ch8_Model_04' at line 78)
##
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Table 8.6
param <- c("tau_fs", "tau_qte25", "tau_qte50", "tau_qte75" )
cbind(
c("No", "No", "Yes", "Yes"),
c("No", "Yes", "Yes", "Yes"),
c("No", "Yes", "Yes", "Yes"),
c("No", "No", "No", "Yes"),
map(
list(fit_mod1, fit_mod2, fit_mod3, fit_mod4),
function(fit_mod) {
fit_mod_summary <- summary(fit_mod)$summary[param, c("mean", "sd")]
fit_mod_summary <- cbind(
formatC(fit_mod_summary[,1], digits = 2, format = "f"),
paste0("(", formatC(fit_mod_summary[,2], digits = 2, format = "f"), ")")
)
return(map(seq(nrow(fit_mod_summary)), ~ fit_mod_summary[.,] %>% as.vector) %>% unlist())
}
) %>%
bind_cols %>%
t()
) %>%
set_rownames(NULL) %>%
kable("html", booktabs = TRUE, align = c("l", rep("c", 11))) %>%
add_header_above(
c(
"Mean\nCovariate\nDependent", "Variance\nTreatment\nSpecific",
"Potential\nOutcome\nIndependent", "Two-\nPart\nModel",
rep(c("Mean", "(S.D.)"), 4)
)
) %>%
add_header_above(
c(" ", " ", " ", " ",
"Mean Effect" = 2, "0.25 quant" = 2, "0.50 quant" = 2, "0.75 quant" = 2
)
) %>%
add_header_above(
c(
" ", " ", " ", " ", " ", " ",
"Effects on Quantiles" = 6
)
) %>%
kable_styling(position = "center")
## New names:
## * NA -> ...1
## * NA -> ...2
## * NA -> ...3
## * NA -> ...4
|
|
|
|
|
|
|
Effects on Quantiles
|
|
|
|
|
|
Mean Effect
|
0.25 quant
|
0.50 quant
|
0.75 quant
|
|
Mean Covariate Dependent
|
Variance Treatment Specific
|
Potential Outcome Independent
|
Two- Part Model
|
Mean
|
(S.D.)
|
Mean
|
(S.D.)
|
Mean
|
(S.D.)
|
Mean
|
(S.D.)
|
|
No
|
No
|
No
|
No
|
1.80
|
(0.64)
|
1.80
|
(0.64)
|
1.80
|
(0.64)
|
1.80
|
(0.64)
|
|
No
|
Yes
|
Yes
|
No
|
1.79
|
(0.49)
|
0.63
|
(0.35)
|
1.65
|
(0.56)
|
3.10
|
(0.63)
|
|
Yes
|
Yes
|
Yes
|
No
|
1.56
|
(0.51)
|
0.42
|
(0.34)
|
1.39
|
(0.56)
|
2.88
|
(0.64)
|
|
Yes
|
Yes
|
Yes
|
Yes
|
1.82
|
(0.88)
|
0.23
|
(0.29)
|
1.01
|
(0.54)
|
1.81
|
(0.75)
|
Table 8.7
summary(fit_mod4)$summary %>% rownames
## [1] "alpha_bin" "beta_bin[1]" "beta_bin[2]" "beta_bin[3]" "beta_bin[4]"
## [6] "beta_bin[5]" "beta_bin[6]" "beta_bin[7]" "beta_bin[8]" "beta_bin[9]"
## [11] "beta_inter_bin[1]" "beta_inter_bin[2]" "beta_inter_bin[3]" "beta_inter_bin[4]" "beta_inter_bin[5]"
## [16] "beta_inter_bin[6]" "beta_inter_bin[7]" "beta_inter_bin[8]" "beta_inter_bin[9]" "tau_bin"
## [21] "alpha_cont" "beta_cont[1]" "beta_cont[2]" "beta_cont[3]" "beta_cont[4]"
## [26] "beta_cont[5]" "beta_cont[6]" "beta_cont[7]" "beta_cont[8]" "beta_cont[9]"
## [31] "beta_inter_cont[1]" "beta_inter_cont[2]" "beta_inter_cont[3]" "beta_inter_cont[4]" "beta_inter_cont[5]"
## [36] "beta_inter_cont[6]" "beta_inter_cont[7]" "beta_inter_cont[8]" "beta_inter_cont[9]" "tau_cont"
## [41] "sigma_t" "sigma_c" "y0_bar" "y1_bar" "tau_fs"
## [46] "log_sigma_c" "log_sigma_t" "tau_qte25" "tau_qte50" "tau_qte75"
## [51] "lp__"
param1 <- c("alpha_cont", paste0("beta_cont[", seq(1,9), "]"))
param2 <- c("tau_cont", paste0("beta_inter_cont[", seq(1,9), "]"))
param3 <- c("alpha_bin", paste0("beta_bin[", seq(1,9), "]"))
param4 <- c("tau_bin", paste0("beta_inter_bin[", seq(1,9), "]"))
cbind(
c(
"intercept", "age", "education", "married",
"nodegree", "black", "earn '74", "earn '74 $>$ 0",
"earn '75", "earn '75 $>$ 0"
),
map(
list(param1, param2, param3, param4),
function(param) {
fit_mod_summary <- summary(fit_mod4)$summary[param, c("mean", "sd")]
fit_mod_summary <- cbind(
formatC(fit_mod_summary[,1], digits = 2, format = "f"),
paste0("(", formatC(fit_mod_summary[,2], digits = 2, format = "f"), ")")
)
} %>%
as_tibble()
) %>%
bind_cols %>%
as.matrix()
) %>%
rbind(
c(
"ln($\\sigma_c$)",
summary(fit_mod4)$summary["log_sigma_c", c("mean")] %>% formatC(digits = 3, format = "f"),
paste0("(", summary(fit_mod4)$summary["log_sigma_c", c("sd")] %>% formatC(digits = 3, format = "f"), ")"),
rep(" ", 6)
),
c(
"ln($\\sigma_t$)",
summary(fit_mod4)$summary["log_sigma_t", c("mean")] %>% formatC(digits = 3, format = "f"),
paste0("(", summary(fit_mod4)$summary["log_sigma_t", c("sd")] %>% formatC(digits = 3, format = "f"), ")"),
rep(" ", 6)
)
) %>%
set_colnames(NULL) %>%
kable("html", booktabs = TRUE, align = c("l", rep("c", 8))) %>%
add_header_above(
c(" ", rep(c("Mean", "(S.D.)"), 4))
) %>%
add_header_above(
c("Covariate", "$\\beta_c$" = 2, "$\\beta_t - \\beta_c$" = 2, "$\\gamma_0$" = 2, "$\\gamma_1 - \\gamma_0$" = 2)
) %>%
kable_styling(position = "center")
## New names:
## * V1 -> V1...1
## * V2 -> V2...2
## * V1 -> V1...3
## * V2 -> V2...4
## * V1 -> V1...5
## * ...
|
Covariate
|
\(\beta_c\)
|
\(\beta_t - \beta_c\)
|
\(\gamma_0\)
|
\(\gamma_1 - \gamma_0\)
|
|
|
Mean
|
(S.D.)
|
Mean
|
(S.D.)
|
Mean
|
(S.D.)
|
Mean
|
(S.D.)
|
|
intercept
|
1.58
|
(0.08)
|
0.07
|
(0.13)
|
0.67
|
(0.14)
|
0.78
|
(0.28)
|
|
age
|
0.02
|
(0.01)
|
-0.02
|
(0.02)
|
-0.01
|
(0.02)
|
0.02
|
(0.03)
|
|
education
|
0.01
|
(0.06)
|
0.01
|
(0.09)
|
-0.05
|
(0.12)
|
0.02
|
(0.18)
|
|
married
|
-0.23
|
(0.25)
|
0.36
|
(0.36)
|
-0.17
|
(0.39)
|
0.91
|
(0.71)
|
|
nodegree
|
-0.01
|
(0.27)
|
-0.24
|
(0.38)
|
-0.28
|
(0.48)
|
-0.25
|
(0.76)
|
|
black
|
-0.44
|
(0.20)
|
0.36
|
(0.31)
|
-1.08
|
(0.43)
|
-0.79
|
(1.02)
|
|
earn ’74
|
-0.01
|
(0.02)
|
0.01
|
(0.03)
|
0.01
|
(0.04)
|
-0.02
|
(0.08)
|
|
earn ’74 \(>\) 0
|
0.18
|
(0.32)
|
-0.57
|
(0.46)
|
1.00
|
(0.55)
|
-3.09
|
(1.14)
|
|
earn ’75
|
0.02
|
(0.04)
|
0.01
|
(0.05)
|
0.00
|
(0.08)
|
0.20
|
(0.17)
|
|
earn ’75 \(>\) 0
|
-0.06
|
(0.29)
|
0.18
|
(0.40)
|
-0.61
|
(0.46)
|
2.15
|
(1.07)
|
|
ln(\(\sigma_c\))
|
0.003
|
(0.057)
|
|
|
|
|
|
|
|
ln(\(\sigma_t\))
|
0.052
|
(0.063)
|
|
|
|
|
|
|


Our mission is to create a better way for customers to discover, engage and shop emerging brands and artists of tomorrow. Enter the wonderous world of Showfields. Our mission is to create a better way for customers to discover, engage and shop emerging brands and artists of tomorrow.


| larv.Engine = class Engine(builtins.object) | ||
| The engine is the glue that puts everything together. It holds: - an instance of a systems list (PriorityList): self.systems - an instance of an entity manager: self.entity_manager - an instance of a group manager: self.group_manager - an instance of an entity_factory: self.entity_factory When updating the game, one should only do a Engine.update() call, as the Engine will be responsable for updating every single system in the right order (given that we insert the systems in the right priority). | ||
Methods defined here:
Data descriptors defined here:
|


Lying about 80nm west of Marseille, it is just 20 miles from the National Park of the Camargue. It is a very busy port with a huge number of boats of all types coming and going. It has a real buzz of its own, forming as it does, one of the gateways to Europe's inland waterways.
Position 43° 23.3'N, 3° 42'E.
It is required that yachts enter this port via the western entrance.
A useful plan of the port area.
The main marina (Port St Claire) is right round to port once through the entrance. The Vieux Port is in the canal opposite Mole Saint-Louis. There is also berthing once through the bridges, in the canal near the train station.
It is possible tie up, free, for a night at the Quai D’Alger if there are no cruise ships in the port. The concession is made for boats supposed to be awaiting the opening of the bridges over the canal leading to the Etang de Thau.
If leaving the boat to over-winter here, be aware of thieves targeting boat electronics and other valuable equipment.
Last updated March 2015.


Pearl barley, or pearled barley, is barley that has been processed to remove its hull and bran. All barley must have its fibrous outer hull removed before it can be eaten; pearl barley is then polished to remove the bran layer.
1 Feb 2017
1 May
3 May
7 May
8 May
8 May


<a> tag (inline, underlined, blue): W3C with accesskey="W", W3C with charset="us-ascii", W3C with hreflang="mn", W3C with rel="Start", W3C with rev="Chapter", W3C with tabindex="1", W3C with type="unknown/unknown"


Well, being bored one night I remembered having about 15 Ultram(tramadol) in my possesion. How they got there is another story. Anyhow, after reading extensively into this topic, (as I do whenever I try any new drug.) I wanted to see what 150 mg would do. I had previously tried 50 and 100 Mg which did nothing. I later found out that they might have if I would've waited long enough before saying 'fuck it' and going to sleep.
So, I went upstairs and popped 3 of the 50mg pills. I glanced at the clock, it read 11:10. I was very excited to see what would happen. I retreated to the computer to read experience reports about other drugs and mess around on the internet. After about 20 minutes I decided I needed a stronger dose, so I popped another one. 2 hours had passed with no effects what so ever, and I totally gave up on Ultram as a recreational drug, thinking any of the effects described in experience reports we're purely a manifestation of the mind. Most likely the placebo effect. As I walked upstairs sad and defeated, the onset hit me. It felt incredably good. I was clearheaded and felt kind of out of myself. Pure Euphoria! I dimmed the lights and turned the TV on low. I felt like I was on top of the world. I would sometimes get lost in a train of thought and could be carried off to whatever I was thinking about. It was like a lucid dream when I closed my eyes.
I turned the TV off and just basked in the wonderfulness of this awesome drug. These effects stayed with me for a few hours, until I felt like I should get some sleep, around 3:30. At first it was difficult but it's always difficult for me to fall asleep. I was out in about 15 minutes. I had very pleasent dreams all through the night. I woke up feeling some slight nausea, and a little tired, but nothing like a hangover or anything. I still felt great, and I never threw up or anything. All in all I must say this experience was definately worth it! It was one of the greatest feelings ever. I would do it again, and probably will.
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.


Innocentin:
Did you finish it? It's the true account of the terrible tragedy that occurred in Hellmann Forest... But it doesn't answer all of my questions... Would you do something for me?
I'm afraid it won't be easy, and you look very tired! Why don't you take a break and come back later when you're refreshed and have had a little more training.
(The Heart of Lidia quest may only be undertaken by a character of level 64 or above.)