{% capture bio %}
I have 16 years commercial experience as a Software Engineer,
Team Lead and Architect. During that time I have received
consistently excellent
feedback about my technical expertise, my pro-active approach and my
clear and honest communication. I am especially passionate about clean code
and applying tried and proven architectural patterns to solve
real world problems.
{% endcapture %}
{{ bio | markdownify }}
Work Experiences
McLaren Applied
Snr SW Engineer (contract)
London/Woking
{% capture mclaren_text %}
Co-architected and co-developed multiple Angular+NodeJS+.NET Core applications.
Frameworks/Languages: **Angular** (v9), **NodeJS**, **.NET Core**,
**SignalR**, **RxJS** (Reactive extensions for JS),
**NgRx** (State manager),
**NestJS** (API Framework for Node), **Angular Schematics**, **TypeScript**, **C#**
* I created a Template Web Application to Kick-Start customer projects. This
consisted of a NodeJS backend handling security and administration and a
frontend in Angular. This included CLI tooling to tailor the application to
the project's needs. The CLI was written in NodeJS using Angular Schematics.
* Implemented 3D telemetry visualisation using ThreeJS and D3. The telemetry data
was received from a .NET Core backend using SignalR. I've also worked on an
Authentication layer in NodeJS against Auth0 using OpenID Connect.
* Co-architected a new authentication layer for the Telemetry Analytics
Platform (.NET Core)
* Worked on the CICD build pipelines (Azure DevOps)
{% endcapture %}
{{ mclaren_text | markdownify }}
Magnitude Internet GmbH
Chief Architect, Co-Founder
Remote
{% capture magnitude_text %}
Designed and co-developed an E-Commerce Solution.
Frameworks/Languages: **Ms Azure Cloud**, **C#**, **ASP.NET MVC 5**,
**ASP Web API 2**, **AngularJS**
* I came in as the Chief Architect for this German Start-up company.
* Designed the cloud based architecture (MS Azure) for their
multi-tiered product and mentored the team on the concepts used.
* Worked together with the CTO on implementing the CICD
pipeline in Atlassian Bamboo. (It was a pain, I'd use Azure DevOps going forward)
{% endcapture %}
{{ magnitude_text | markdownify }}
Invenias
Snr SW Engineer (contract)
Reading
{% capture invenias_text %}
Re-implementation of Web-Based CMS in Angular 7.
Frameworks/Languages: **Angular** (v7), **RxJS** (Reactive extensions for JS),
**NgRx** (State manager), **C#**, **Visual Studio 2017**,
**Visual Studio Code**.
This client was struggling with performance issues, maintainability and
stability. I’ve taken the following steps to move them forward:
* Start to re-implement the application using several new concepts in
a side-by-side way, so the improvements can be seen immediately.
Many new features were implemented along the way.
* Introduce NgRx for client-side state management and caching.
* Migrate from Angular 4 to Angular 7 to take advantage of
out-of-the-box performance improvements.
* Move them to the new Angular CLI build system. This took dev builds
from 8 minutes down to 2.
* Set up code linting to highlight anti-patterns and use of deprecated features.
* Mentor the team on all the new concepts so that they can to move forward.
{% endcapture %}
{{ invenias_text | markdownify }}
Fiserv
Snr SW Engineer (contract)
Slough
Re-implementation of Risk Assessment Software in Angular 5.
Tools/Languages: C#, WebAPI, Angular (v5), RxJS (Reactive extensions for JS), ngrx/store (State manager), Visual Studio 2015, Visual Studio Code, MS SQL Server.
The old version of the FCRM risk assessment software was implemented in Silverlight and had to be re-implemented in Angular. This included writing a REST API wrapper for the legacy server-side logic using WebAPI. The client-side application was then created from scratch using Angular.
I did some work on the server-side back-end, but I mainly focused on the client-side Angular application and ended up writing about 80% of the app on my own. There was a heavy focus on observability (data changes in one part of the UI affecting other parts), so we heavily used the ngrx/store, ngrx/entities and ngrx/effects packages to manage and observe client-side state changes.
Knight Frank
Software Engineer (contract)
London
Extending and updating Knight Frank's web presence.
Tools/Languages: C#, ASP MVC, AngularJS, ASP WebApi, WebForms, Visual Studio 2015, MS SQL Server
I have written multiple extensions and fixes to the current website. Noteworthy:
A new property search for the Knight Frank site, written in Angular:
www.knightfrank.com
I have trained some of Couchbase's customers in the use of the Couchbase C SDK (libcouchbase).
Tools/Languages: C++, Qt 5, libcouchbase, Qt Designer
These are one-day trainings which I have prepared together with an employee of Couchbase and held on-site in various locations. So far I've given this training six times to consistently positive feedback.
Momentum Coach Hire
Senior Software Engineer
White Waltham
I have been working for Momentum Coach Hire, an ambitious little company in the Coach Hire and Event Organisation business. There, I have created a variety of new web sites and improved existing ones. I have also been promoted to IT Director and am responsible for the migration of the entire company (50 employees) onto Microsoft Office 365.
Symantec
Senior Software Engineer
Reading
I have been working as a Senior Software Engineer for Symantec UK Ltd. I was in the Research and Development team for Symantec Enterprise Vault, an email archiving solution written in C# and C++. In my time there I have performed the following tasks:
Completed or am working on a number of small to medium projects including new features and architectural enhancements to improve resilience.
For these projects, I have created the functional specification and software design documents and have implemented them on my own or as part of a team.
Main tools/skills used: OOA OOD Database Design UML MS Visual Studio 2008 C# C++ XSLT SQL
Fixed many defects in different code streams as part of learning the project architecture
Received outstanding performance reviews for pro-actively driving architectural improvements and my excellent communication and team work
Ingres Europe Ltd (now Actian)
Consultant
Slough
With Ingres I worked as a permanently employed Consultant. I was involved in the following customer projects:
Head Developer and Technical Team Lead for an open source project adding playback and recording capabilities to the Ingres database.
Using the following skills/tools:
Heavy NPTL multi-threading on UNIX using C (tested with 1000s of threads)
GCC, GNU Automake, Netbeans IDE
Lexical processing in C# running on .NET and Mono
Ported from Linux to Solaris, HPUX, AIX, Windows
Held a presentation about it at the 2009 UK Ingres User Association
Head Developer for porting the MS Excel based reporting for Newline Underwriting Ltd to Jasper Reports:
Working on site with the customer in an agile environment.
Talend Open Studio ETL, Jasper iReport
SQL Database Design and Implementation (Ingres Database)
SQL overnight number crunching scripts using BASH
Created a Data entry Website using Grails including user management and authentication
Developer in a C# project for the MET Police.
On site consulting at BNP Paribas
HM Informatik AG
Technical Team Lead
Ilmenau, Germany
{% capture hm_text %}
HM Informatik is a very agile company focused on small to medium size customer projects. There were many projects being worked on simultaneously and I have fulfilled varying roles within them, like:
- Head Developer / Architect
- Technical Team Lead
- Project Manager
- Trainer
Within those roles, the following tasks are noteworthy:
- I was responsible for the full project Life Cycle. Statement of Work, Analysis, Design, Implementation, Integration, Support for multiple projects.
- Managed teams up to 5 people.
- Was responsible for the in-house migration from MS Visual SourceSafe to Subversion
- Managing according to the V-Model (German equivalent of PRINCE 2)
- Have given a lot of successful technical trainings with up to 20 trainees
- Given technical presentations at the CA Infoexchange 2004 and 2005 with audiences of 100+ people.
Technical Achievements:
- Lightweight open source ORM in C# using the .NET Reflection API
- Head Developer, Team Leader and SQL Database Architect for a Logistics Module for a large European CD/DVD manufacturer written in OpenROAD interfacing SAP/R3 using C++, C# and SOAP web services with an Ingres Database back end.
- I have been in the development team of the application for measuring and approval of ship licenses (MOT for ships if you like) for the German Central Ship Inspection Commission (ZSUK). This Application was originally written in C++ and later ported to Java.
- As Head Developer and Team Leader for a Reporting tool for the German Armed Forces I created an interface to a live 3D simulation engine to gather statistics. C#, SQL Database Design.
- I have written a reporting server daemon which enables applications to use Crystal Reports through a very easy database API. Thinking Instruments was licensing this tool to several customers. The server was written in C#.
{% endcapture %}
{{ hm_text | markdownify }}
Certifications
Microsoft Certified Azure Solutions Architect Expert
Earning the Azure Solutions Architect Expert certification demonstrates skills and knowledge to advise stakeholders and translate business requirements into secure, scalable, and reliable solutions. Candidates have advanced experience and knowledge across various aspects of IT operations, including networking, virtualization, identity, security, business continuity, disaster recovery, data management, budgeting, and governance - managing how decisions in each area affects an overall solution.


Ecclesiastes 10:16
Woe to thee, O land, when thy king is a child, and thy princes eat in the morning!


Class StreamSocketConnectionPool
The default implementation of the ConnectionPoolInterface using stream
sockets.
-
PHPFastCGI\FastCGIDaemon\Connection\StreamSocketConnectionPool
implements
PHPFastCGI\FastCGIDaemon\Connection\ConnectionPoolInterface
Methods summary
public
|
#
__construct( resource $socket )
Constructor.
Parameters
- $socket
- The stream socket to accept connections from
|
public
|
#
operate( PHPFastCGI\FastCGIDaemon\ConnectionHandler\ConnectionHandlerFactoryInterface $connectionHandlerFactory, float $timeoutLoop )
Uses the connection handler factory to instantiate connection handlers
when new connections are made to the connection pool. Monitors current
connections and triggers them when read operations will not block.
Uses the connection handler factory to instantiate connection handlers
when new connections are made to the connection pool. Monitors current
connections and triggers them when read operations will not block.
Parameters
- $connectionHandlerFactory
- The factory used to create connection handlers
- $timeoutLoop
- The timeout value to use when waiting for activity on incoming connections
Implementation of
|
public
|
#
shutdown( )
Shutdown the connection pool cleanly. Usually triggered following a
SIGINT.
Shutdown the connection pool cleanly. Usually triggered following a
SIGINT.
Implementation of
|


WELCOME TO TORTILA PIZZA HUT
Did you know
pizza
is from an Italian word
'pitta' which means pie or from 'bizzo' which means
bite.


Some 55 gallon oil drums
A customer sent me some photos of a group of beat up rusty 55 gallon drums. He wanted to know if I would paint and weather some of our drums for him. Of course we will do that! We can do it for you, too.
This group represents the drums that are in better condition than the rest. Once the castings were cleaned up, washed and primed with automotive Gray primer, they were given a spray coat of ruddy brown or oxide red. This was allowed to dry then Bragdon rust color powders were applied then sealed with clear. The blue and wine color drums were then painted their final colors; the orange drum was treated differantly...... .Read on below.
To achieve the nasty rusted look of the more battered drums they were prepared as
above, but before painting the final color the drums had a coat of a craft store
product called “Weathered Wood” brushed on. This is a clear gel-
This was a fun project. Weathering is a creative endeavor that gives a model that
realistic touch. We always look forward to doing projects like this for our customers
so we hope you will consider some weathered drums for your railroad. We used 1 set
of FA16-


This is intended to provide a set of online crypto currency helper tools.
Investment Calculator
Limit Amount
Cost Price
Volume
Fee %
0
Profit Calculator
Investment 0
Cost Price
Volume
Fee %
Add a group
Output
Selling Price
0


Boom, next video!
was created by select because he loves funny videos.
The idea was out there for a long time but not enough motivation to do it. Then 5by came along
and fulfilled all his dreams. The Wow channel was everthing he hoped for ... but oh crule
fate, 5by disappeared.
He still wanted to watch funny videos with his buddies and so BoomNext was created on a few weekends.
If you like it give it a star on github!.
If you found bugs, you have another site that has more cool videos that should be added, go to the github
issue tracker and add a comment.


The sort utility performs one of the following functions:
Comparisons are based on one or more sort keys/fields extracted from each line of input. If no sort keys/fields are specified, comparisons are based on the entire line up to, but not including, the terminating <newline>. All comparisons are performed using the collating sequence of the current locale.
Sorting is stable, that is, the ordering of input lines is preserved if they are considered equal according to the current comparison criteria (if two lines originate from different input files, the index of the file in the input arguments list defines the ordering of the lines).


Resume
Saint Joseph’s University, Philadelphia, Pa — Bachelor of Arts, Graduation Date: May 2015
Communications major, Marketing minor, Cumulative GPA: 3.06
Communications & Marketing courses:
College Media Matters, Civic Media, Digital Storytelling, Communications Theory and Practice, Communications Ethics, Web Design and Development, Digital Photography 1, Journalism, Integrated Marketing and Communications, Organizational Behavior, Principles of Marketing, Consumer and Buyer Behavior, Digital Filmmaking, Intro to Microeconomics, Intro to Business Statistics, History of Pop Culture, Marketing Strategy, Marketing Research, Social Media Marketing
Marketing Intern, Aspire Ventures; Lancaster, PA — May 2014-Present
Correspondent, College Media Matters Blog; Philadelphia, PA — September 2014-Present
Features Writer, “The Hawk” Student Newspaper; Philadelphia, PA — September 2013-Present
Social Media Intern, Collectible Knits and Gifts; Philadelphia, PA — November 2013-May 2014
Phi Sigma Sigma (Founder, Historian Committee, PR Committee, manage Instagram), Club Basketball (CoCaptain), Saint Joseph’s University American Marketing Association, Women for Women, Best of the Nest, The Calliope Joy Foundation, create and maintain fitness blog at kaylasoders.com


50$
This is a cheap pillow.
100$
This is a cheap and casual pillow.
200$
This is a cheap, casual and comfy pillow.


Part of pymine.api.minekey View In Hierarchy
| Method | __init__ | Create a nu-format minekey. |
| Class Method | create_feedmk | assuming 'feed' is a valid Feed object instance, return a |
| Method | __str__ | returns the minekey information WITHOUT hmac, in the following format: |
| Method | mkerr | create a diag string to drop into an exception |
| Method | validate | does a variety of sanity checks on self and throws a |
| Method | get_hmac | computes and returns the hmac for this minekey |
| Method | get_feed | return a (cached) copy of the feed object corresponding to this minekey |
| Method | get_item | Undocumented |
| Method | get_ext | return a viable extention for this key |
| Method | key | validates, and then returns the minekey information WITH hmac, in the following format: |
| Method | permalink | takes the result of self.key() and embeds / returns it in a |
| Method | http_path | returns the http://host.domain:port/ for this mine, for template use |
| Method | clone | clone this minekey for further futzing |
| Method | spawn_data | from this minekey, spawn a new minekey object for the same |
| Method | spawn_icon | from this minekey, spawn a new minekey object for the same |
| Method | spawn_icon_self | return spawn_icon for self |
| Method | spawn_submit | from this minekey, spawn a new minekey object for comment |
| Method | spawn_submit_self | from this minekey, spawn a new minekey object for comment |
| Method | access_check | checks the per-feed security policy and enforces it |
| Method | response | returns the appropriate http response for this minekey |
| Method | rewrite_html | using the context of this minekey, rewrite the blob of HTML |
request: request object for URL-generation purposes
kwargs include: type: one of ('data', 'icon', 'submit') fid: integer feed id > 0 fversion: integer feed version number > 0 iid: integer item id >= 0 depth: integer depth > 0 ext: regexp ^\w+$ hmac: hmac against which to check this minekey
if either 'hmac' is supplied, or if kwargs[enforce_hmac_check] evaluates to True, a hmac test is performed; this option is provided to allow the programmer to ENFORCE a hmac check, just in case something higher-up permits code to slide through that sets hmac=None/False/0
There once was a time when Feed objects were called 'Relations' because they held data pertinent to the relationship between a mine user and one of his subscribers; this concept seemed to the programmer to be distinct from (say) the feeds that would be generated.
However this terminology was deemed "confusing" by the Mine design team and so now we have Feed objects (one thing) which generate feeds (another thing entirely) - obvious, n'est-ce pas?
fid/fversion/iid/depth/type
for a minekey such as "HMAC/42/1/11/2/data.dat" - the validation covers everything from "HMAC" to "data" inclusive; the trailing ".dat" is considered advisory.
if enforce_hmac_check evaluates as True (default False) then the computed hmac is compared against the hmac declared at instantiation; if there is a difference, an exception is thrown.
hmac/[fid/fversion/iid/depth/type].ext
...the [central portion] being generated by __str__() and protected by hmac
if you do futz manually, remember to do minekey.validate()
HREF=/api/data/99 HREF=/api/data/99/dummy.html HREF='/api/data/99/dummy.html' HREF="/api/data/99" SRC=/api/icon/99/dummy.png SRC='/api/icon/99' SRC="/api/icon/99/dummy.png"
...where 99 is an example iid; the rewriter replacing the 99 with the results of self.spawn_data(iid).permalink() - in other words a URL customised to retreive that item/iid with decremented depth.
The rewriter tries to be semi-clever, but not very, alas; it does not check that we are currently inside a <TAG> before rewriting such strings. When BeautifulSoup comes in, that will change.
if the /api/icon/99/dummy.png URL is prefixed with either a single or double-quote, the rewriter checks that the substring to be rewritten ends with a corresponding quote; this is inadequate but should hope with unbalanced quotation marks during development.
if the URL does NOT begin with a quote character, the rewriter ignores what the last character of the matched string, irrespective of whether it is a quote character or not.
if any sanity-check fails, the rewriter simply does not rewrite the HTML.


Ui Operators¶
Set this property’s current value as the new default
Presses active button
Parameters: skip_depressed (boolean, (optional)) – Skip Depressed
Unsets the text of the active button
Create a new driver with this property as input, and copy it to the clipboard. Use Paste Driver to add it to the target property, or Paste Driver Variables to extend an existing driver
Copy the RNA data path for this property to the clipboard
Parameters: full_path (boolean, (optional)) – full_path, Copy full data path
Copy the Python command matching this button
Copy property from this object to selected objects or bones
Parameters: all (boolean, (optional)) – All, Copy to selected all elements of the array
-
bpy.ops.ui.
drop_color
(color=(0.0, 0.0, 0.0), gamma=False)¶ Drop colors to buttons
Parameters: - color (float array of 3 items in [0, inf], (optional)) – Color, Source color
- gamma (boolean, (optional)) – Gamma Corrected, The source color is gamma corrected
-
bpy.ops.ui.
editsource
()¶ Edit UI source code of the active button
-
bpy.ops.ui.
edittranslation_init
()¶ Edit i18n in current language for the active button
-
bpy.ops.ui.
eyedropper_color
(use_accumulate=True)¶ Sample a color from the Blender window to store in a property
Parameters: use_accumulate (boolean, (optional)) – Accumulate
-
bpy.ops.ui.
eyedropper_colorramp
()¶ Sample a color band
-
bpy.ops.ui.
eyedropper_colorramp_point
()¶ Point-sample a color band
-
bpy.ops.ui.
eyedropper_depth
()¶ Sample depth from the 3D view
-
bpy.ops.ui.
eyedropper_driver
(mapping_type='SINGLE_MANY')¶ Pick a property to use as a driver target
Parameters: mapping_type (enum in ['SINGLE_MANY', 'DIRECT', 'MATCH', 'NONE_ALL', 'NONE_SINGLE'], (optional)) – Mapping Type, Method used to match target and driven properties
SINGLE_MANY
All from Target, Drive all components of this property using the target picked.DIRECT
Single from Target, Drive this component of this property using the target picked.MATCH
Match Indices, Create drivers for each pair of corresponding elements.NONE_ALL
Manually Create Later, Create drivers for all properties without assigning any targets yet.NONE_SINGLE
Manually Create Later (Single), Create driver for this property only and without assigning any targets yet.
-
bpy.ops.ui.
eyedropper_gpencil_color
()¶ Sample a color from the Blender Window and create Grease Pencil material
-
bpy.ops.ui.
eyedropper_id
()¶ Sample a data-block from the 3D View to store in a property
Switch to the target object or bone
Remove an override operation
Parameters: all (boolean, (optional)) – All, Reset to default values all elements of the array
Create an override operation, or set the type of an existing one
Parameters: - all (boolean, (optional)) – All, Reset to default values all elements of the array
- type (enum in ['NOOP', 'REPLACE', 'DIFFERENCE', 'FACTOR'], (optional)) –
Type, Type of override operation
NOOP
NoOp, ‘No-Operation’, place holder preventing automatic override to ever affect the property.REPLACE
Replace, Completely replace value from linked data by local one.DIFFERENCE
Difference, Store difference to linked data value.FACTOR
Factor, Store factor to linked data value (useful e.g. for scale).
-
bpy.ops.ui.
reloadtranslation
()¶ Force a full reload of UI translation
Reset this property’s value to its default value
Parameters: all (boolean, (optional)) – All, Reset to default values all elements of the array
Clear the property and use default or generated value in operators


Questions:
+Add
Short Answer Question
Long Answer Question
MCQ
CheckBox
#d9efed
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Type :
Short
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Type :
Long
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New Farm Loan Discovery Tool Connects You With Loan Options Tailored to Your Needs.
Fill the form of Farmer Aider farm loans that might be right for you. Farmer Aider also offers other funding opportunities to help farmers, ranchers, and forest landowners finance their business.


I've bought an ounce of Virola theidora shredded bark online three years ago. I've snorted some, smoked some and made a tea with very mild effects, then forgot about it.
Recently I remember that I had some that I powdered in order to try an alcohol extraction. I dont know how much was left, mabe 15 grams (?). I made a tea with all that was left.
I drank the resulting three cups over the course of one hour. Soon after having drunk the first one, I felt somehow drunk and happy and couldnt stop laughing. My mind was sped up and I was thinking about global conspiracies, media manipulation and so on. Things relate very easily and all was logical. I felt also stronger and self confident. I smoked pot, and it added to the experience.
I reboiled the powder and it produced a lighter red tea. I drank the three cups and it seemed to boost things a little. After a while, I was still happy and inebriated, just less high and I didnt laugh anymore, but still was feeling fine.
I was hungry, so I ate some cheese. Eating made me higher than ever before for 20 minutes. After that, I remembered that DMT (a main chemical of Epena) is not orally active. As it did produce a rather strong effect, I thought at that time that the bark either was not Epena or contained an MAOI, which could lead to problems with cheese.
My stomach was a bit upset, but that was all. I searched the web for info regarding the presence of a MAOI in Virola bark. The documents I found were unclear and I dont know if they were regarding Virola theidora or other species.
Except the bearable light stomach pain, I didnt feel ill effects. the peak lasted between one and two hours. After effects were very long lasting. The next day, I woke up hungover and very very happy, balanced and in a positive mood. I was depressed since a week, feeling weak and paranoid but the Epena cured it all. The hangover cleared during the day, but the well-being feeling remained. It took place two days ago, and I'm still feeling fine.
Having a rather strong high and persistant antidepressant effects was unexpected.
I ordered a large batch of crude bark and concentrated extract to research further with this great ally. I hope to have an entheogenic experience.
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.


In this talk we will cover how we apply QA in Emergya for Python projects, covering from the inception phase to the delivery phase.
We will see a showcase of a real python project to learn by examples (code guidelines definition, Continuous Integration system, Continuous Unit and Functional testing, Performance testing and Continuous Delivery.
We will focus the talk in how a team should manage a Python project from the QA side, showing how a team of developers and QA build a real team.
Alejandro started working as a web developer in 2006 after starting his Computer Engineering & Information Systems degree at Pablo Olavide University (Seville). After spending a few years developing in PHP and JavaScript, he also started working with Java.
He moved to the UK where he worked as a developer, learning and enjoying his life there. During the ending of 2012 and the beginning of 2013, he worked at klicap - nowadays acquired by CloudBees. There he discovered how amazing Continous Integration and contributing to the community could be.
In June, 2013 he started working at Emergya as a developer in an international Drupal project but he soon discovered his real passion was the QA world. Since that moment, he has been working at Emergya building the QA area from scratch with José Antonio Sánchez - nowadays he is the Emergya QA Area leader - and also as a Tech Project Manager.
He has been the QA lecturer at Pablo de Olavide University since 2013 because he loves transfering knowledge and bringing real world problems into the university.
He started working as Project Manager but he never forgot the QA role and so strong was his belief in the importance of QA for a project that the Emergya QA Team has been growing. With the help of some Emergya colleagues, in less than a year, the QA Team has increased by more than 12 people and they are working on the majority of the projects.
He worked as a developer in Drupal, Java, Ionic and Angular projects while always keeping an eye on the QA side, helping the QA team on the projects and also coordinating the QA Area in Emergya where, with José Antonio Sánchez, Óscar Castaño and Francisco M. Araque, they make a great team.
Nowadays, he supports Alejandro Laó in the huge challenge of applying and modeling the QA process in the Python and Django projects lifecycle.
He enjoys spending his free time with relatives and friends, playing football, reading books, learning and keeping always keeping in touch with tech trends and, of course, contributing to the QA community and also to the Ionic and Angular one. He is known as @agomezmoron in the community and he is a tech and QA lover.
“Teamwork doesn’t take strength to win. It takes the true heart of the team to win.” Emily Voyles


Building out map generation and decoupling chunks from generation.
I spent some time building a skeleton for map generation. The basic organization is the sim manager has a
ChunkSource
which it can use to request chunks. The
ChunkSource
holds all the random/noise information and determines which
cell generator will be used for a given chunk. When a chunk is created it is
given a generator instance which it calls to generate cells. The generator knows
how to pick cells given their global coordinates and access to the random/noise
generators in the ChunkSource
.
It is all just minimally mocked up for now, for instance there's currently only
one generator called Grassland
which is used for every chunk. By building
these parts I discovered additional requirements for the system I have in
mind. Because of how I am handling generation I would like to have one central
place where all possible terrain types are enumerated, which is then used by all
the generators. To do this I created a Materials
singleton which can map
between material names and simulation ids:
class Materials
{
public Dictionary<string, int> AllIds;
public Dictionary<int, string> AllNames;
Dictionary<string, int> _pitIds;
Dictionary<int, string> _pitNames;
Dictionary<string, int> _floorIds;
Dictionary<int, string> _floorNames;
Dictionary<string, int> _wallIds;
Dictionary<int, string> _wallNames;
private static readonly Materials instance = new Materials();
private Materials()
{
int index = 1;
index = InitOther(index);
index = InitPits(index);
index = InitFloors(index);
index = InitWalls(index);
}
int InitOther(int index)
{
AllIds = new Dictionary<string, int>
{
{"Void", 0 },
{"Beach", index },
};
AllNames = new Dictionary<int, string>();
int id;
string name;
foreach (var pair in AllIds)
{
name = pair.Key;
id = pair.Value;
AllNames[id] = name;
}
return index + 1;
}
int InitPits(int index)
{
// Increment index for each type, left shift 8+n for each sub-type
_pitIds = new Dictionary<string, int>
{
{"FreshWater.Brick", (index + 0) },
{"FreshWater.Rock", (index + 0) << 8 },
{"FreshWater.Smooth", (index + 0) << 9 },
{"SaltWater.Brick", (index + 1) },
{"SaltWater.Rock", (index + 1) << 8 },
{"SaltWater.Smooth", (index + 1) << 9 },
{"PoisonWater.Brick", (index + 2) },
{"PoisonWater.Rock", (index + 2) << 8 },
{"PoisonWater.Smooth", (index + 2) << 9 },
{"Lava", (index + 3) },
};
_pitNames = new Dictionary<int, string>();
int id;
string name;
foreach (var pair in _pitIds)
{
name = pair.Key;
id = pair.Value;
// Note each pair is added to the All materials maps here
AllIds["Pit." + name] = id;
AllNames[id] = "Pit." + name;
_pitNames[id] = name;
}
return index + 4;
}
int InitFloors(int index)
{
...
}
int InitWalls(int index)
{
...
}
static public int GetPit(string name) =>
instance._pitIds.ContainsKey(name) ? instance._pitIds[name] : 0;
static public string GetPit(int id) =>
instance._pitNames.ContainsKey(id) ? instance._pitNames[id] : "";
static public int GetFloor(string name) =>
instance._floorIds.ContainsKey(name) ? instance._floorIds[name] : 0;
static public string GetFloor(int id) =>
instance._floorNames.ContainsKey(id) ? instance._floorNames[id] : "";
static public int GetWall(string name) =>
instance._wallIds.ContainsKey(name) ? instance._wallIds[name] : 0;
static public string GetWall(int id) =>
instance._wallNames.ContainsKey(id) ? instance._wallNames[id] : "";
static public int GetId(string name) =>
instance.AllIds.ContainsKey(name) ? instance.AllIds[name] : 0;
static public string GetName(int id) =>
instance.AllNames.ContainsKey(id) ? instance.AllNames[id] : "";
}
It will be pretty trivial to transition these to JSON data files, but for the small sample I'm implementing I just hard-coded the material lists. Materials can be accessed either by their type (pit/floor/wall) or in the global mapping by qualified name. Here is how I am currently using them:
class Grassland: AbstractGenerator
{
Dictionary<int, string> _mats;
public Grassland(ChunkSource source): base(source)
{
}
override public IDictionary<int, string> UsedMaterials
{
get
{
if (_mats == null)
{
_mats = new Dictionary<int, string>
{
{0, "Void" }
};
// This array may be replaced by a probabilities field
// if I can figure out a sane way to store probabilities
var ids = new int[]
{
Materials.GetPit("FreshWater.Rock"),
Materials.GetId("Beach"),
Materials.GetFloor("Dirt"),
Materials.GetFloor("Dirt.Grass"),
};
foreach (var id in ids)
{
_mats[id] = Materials.GetName(id);
};
}
return _mats;
}
}
public override Cell GetCell(Vector coord)
{
var local = Chunk.LocalCoord(coord);
var value = _source.SampleCell(coord);
var floor = PickFloor(value);
//var fill = (0.5 <= value && value <= 0.6) ? "wall" : "";
return new Cell(floor, 0);
}
int PickFloor(double value)
{
// Given a number [-1.0 1.0] map ranges to floor material
if (value < -.8)
return Materials.GetPit("FreshWater.Rock");
if (value < -.4)
return Materials.GetId("Beach");
if (value < -.25)
return Materials.GetFloor("Dirt");
if (value >= -.25)
return Materials.GetFloor("Dirt.Grass");
return 0;
}
}
I think I have an idea how I can encode the ranges used to pick materials, which will clean this code up a lot. One challenge I'm going to run into when I expand this to additional terrain types is how to transition between regions. If I can make the generation a little more data-driven though I can just create interpolated terrain types to smooth the transitions. Here is an example of the generated terrain:
One of the first problems revealed by this terrain generator was a deficiency in my graphic tile selection. Note all the extra "Beach" borders between the dirt and the grass. The dirt's border transitions to sand, but it is used anywhere a dirt tile is bordered by a non-dirt tile. The grass border transitions to dirt, so it would merge seamlessly if the dirt weren't adding its own border. I think I need some kind of affiliate system in the tile selection, where dirt will consider grass to be a match, but grass will not match dirt.
The next problem I have is the slope of the noise gradients. I had to tweak both the noise frequency and my material bands repeatedly to get the above result, and it still isn't satisfactory. My first thought when I discovered the problem was to apply a function over the noise, in this instance something that will tighten up the areas closer to -1 and gradually flatten everything above -0.4. It turns out SharpNoise has a module for just that purpose. Applying a function to the entire noise field for each generator will exacerbate the problems in transition areas, so I need to allow interpolation of those functions in region boundaries too.
I am loosing momentum a little with this project. When I can focus on it again I will probably jump back to the display side. Now that I have infinite expanses of terrain the ability to move the view would be nice.
GitHub commit 6b0f94325.


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A Gentle Introduction to Deep Sets and Neural Processes
In this post, I will discuss two topics that I have been thinking a lot about recently: Deep Sets and Neural Processes. I'll lay out what I see as the important bits of these models, and hopefully provide some intuition as to why they are useful. My motivation will, in particular, focus on meta-learning. I'll start with a brief introduction to meta-learning, which will serve the dual purpose of (i) motivating everything I'll talk about later, and (ii) will allow me to introduce some terminology and objects that we'll need for the rest of the post.
Quick Background on Meta-Learning
Let's first recall what the (supervised) meta-learning setting is. In the standard supervised learning setting, we are often interested in learning / approximating a function ($f$) that maps inputs ($x$) to outputs ($y$). A supervised learning algorithm ($L$) may be considered an algorithm that, given a dataset of such input-output pairs, returns a function approximator ($\hat f$). If $L$ is a good algorithm, $\hat f \approx f$ in some meaningful sense.
Fig. 1: Observations from a nonlinear function are passed to a learning algorithm. In this visualization I am using a Gaussian Process (GP) as the learning algorithm $L$. GPs being probabilistic models, this produces a distribution over $\hat{f}$.
In the meta-learning setting, rather than assuming we have access to one such (potentially very large) dataset, we assume that our dataset is comprised of many tasks, each containing a context set ($D_c$) and a target set ($D_\tau$). Each of these, in turn, contain a variable number of input-output pairs. Our assumption is that while the mapping between inputs and outputs may differ across tasks, the tasks share some statistical properties that, when modelled appropriately, should improve the overall performance of the learning algorithms.
So the goal of meta-learning is to learn to produce the black-box algorithm that maps from datasets to function approximators. In other words, our goal is to use our dataset of tasks to train a model that, at test time accepts new training sets, and produces function approximators that perform well on unseen data from the same task. Intuitively, the meta-learning algorithm learns a learning algorithm that is appropriate for all of the observed tasks. A good meta-learning algorithm results in a learning algorithm that has desirable properties for tasks similar to those in our training set (e.g., produce good function approximators, are sample-efficient, etc’). This is where the popular description of meta-learning as learning to learn comes from.
Fig. 2: A large collection of few-shot tasks is provided during meta-training. The meta-learner learns to map few-shot tasks to meaningful predictive distributions.
One of the most compelling motivations for meta-learning is data efficiency. Neural networks notoriously require large datasets to learn from. However, it has been observed1 that humans, for example, are able to learn from just a handful of examples. This is a major difference between human intelligence and our current machine learning systems, and an extremely attractive feature to aspire for with our learning systems. Sample-efficient learners would be wildly useful in many applications such as robotics, reinforcement learning, and others. This line of thinking has lead to the sub-field of research called few-shot learning. In this setting, models are only provided a handful of examples of the task they are required to perform. Meta-learning is a particularly successful approach to designing models that can achieve this.
Fig. 3: Two examples of few-shot learning problems. (Left) An example image is shown (in the red box). All images of the same object must be identified from the set on the bottom. (Right) Same setup, now with characters from an alphabet. Images borrowed from [1].
The Neural Process Family
Let us know focus our attention on the Neural Process (NP) family2. NPs are a recently introduced family of models for probabilistic meta-learning. While there are interesting latent variable variants, in this blog post I'll focus on conditional neural processes (CNPs), which are a simple deterministic variant of NPs.
CNPs leverage a series of mappings to directly model the predictive distribution at some location of interest ($x_t$) conditioned on a context set. Mathematically, for a Gaussian predictive likelihood we can express this as
$$ p(y_t | x_t, D_c) = \mathcal{N} \left(y_t; \mu_\theta(x_t, D_c), \sigma^2_\theta(x_t, D_c) \right), \tag{1}\label{eq1} $$
where $x_t$ is the input we wish to make a prediction at, and $D_c$ is the context set to condition on. The computational structure of NPs is best understood from an encoder-decoder perspective: computationally, we can visualize this model as follows:
Fig. 3: Computational diagram of CNPs. On the left, $(x_i, y_i)$ represent the context set. The $e$ block is the encoder, the $a$-circle the pooling operation, and the $d$ block is the decoder. Output is a Gaussian distribution over $y_t$ for the $x_t$ passed to the decoder. Image borrowed from the DeepMind NP repository3.
Note the important form of the encoder: it first applies the same mapping ($e$) to each of the $(x_i, y_i)$ pairs in the context set, producing a representation ($r_i$). A natural choice is to implement $e$ with a neural network. Then, a pooling operation $a$ is applied to the representations, yielding a single, vector-valued representation of the context set, denoted $r$. This pooling operation is very important, and we'll return to it later. Finally, to make predictions at $x_t$, we concatenate it to $r$, and pass this through our decoder, $d$, which maps to a mean and variance for the predictive distribution. A natural choice for $d$ is also a neural network.
That's it! Given a dataset of tasks, we can now train the parameters of the model (the weights $\theta$ of the encoder and decoder) to maximize the log-likelihood i.e., the $\log$ of Eq. $\eqref{eq1}$: $$ \theta^\ast = \underset{\theta \in \Theta}{\text{argmax}}\ \mathbb{E}_\tau \left[ \sum \log p \left(y | x, D_c \right) \right], $$
where we can take the expectation over tasks $\tau$ with our dataset, and the inner summation is over the $(x_t, y_t)$ pairs in the target set. In practice, at every iteration we sample a batch of tasks from our dataset of tasks, and (if they are not already) partition them into context and target sets. We then pass each context set through $e$ to produce $r$. Each $x_t$ in the target sets is concatenated with $r$, and this is passed through the decoder to get the predictive distribution. The objective function for the iteration is then the sum over the likelihoods in the target sets, averaged across the tasks. This is fully differentiable wrt the model parameters, so we can use standard optimizers and auto-differentiation to train the model.
Below, I show the training progression of a simple CNP trained on samples from a Gaussian Process with an EQ kernel after 0, 20, and 40 epochs of training.
Fig. 4: Snapshots from CNP training procedure after 0, 20, and 40 epochs. Each epoch iterates over 1024 randomly sampled tasks.
One thing you might immediately notice is that the CNP seems to underfit compared to the oracle GP (by oracle, I mean using the ground truth kernel). On the one hand, this is true: the uncertainty is not tight, even when many data points are observed. One the other hand, the GP has far more domain knowledge (the exact kernel used to generate the data), and so it is not clear how certain the CNP really should be. Regardless, CNPs suffer from a severe under-fitting problem. In my next post, I will explore several interesting additions to the NP-family that are aimed at addressing this issue.
Deep Sets
OK, let's dive a little deeper into the inner workings of the CNP. A useful way to think about them is as follows. Our decoder is a standard neural network that maps $X \to Y$, with one small tweak: we condition it on an additional input $r$, which is a dataset-specific representation. The encoder's job is really just to embed datasets into an appropriate vector space. CNPs specify such an embedding, and provide a way to train this embedding end-to-end with a predictive model. However, one might ask – is this the “correct” form for such an embedding?
So the question that really arises is – what does a function that embeds datasets into a vector space look like? This is quite different from the standard machine learning model, where we expect inputs to be an instance from some space, typically a vector space (feature vectors, images, etc’), or at worst – sequences (e.g., for RNNs). Here what we want is a function approximator that accepts as inputs sets.
What are the properties of sets, and what properties would we want such a function to have? Well, the first problem is that sets have varying sizes, and we would like to be able to handle arbitrarily-sized sets. The second key issue is that, by definition, sets have no order. As such, any module operating on sets must be invariant to the order of the elements to be considered valid. This property is known as permutation invariance, and is a key concept in the rapidly growing literature on modelling and representation learning with sets.
Now, this is a bit of sticky situation: on the one hand, we've declared that sets have no ordering. On the other hand, I am now stating that our module must be invariant to their ordering. The resolution to this stickiness is that in practice, our algorithms must operate on representations of sets, and must process these sequentially. Thus, it would be more accurate to say that we are construcing modules that process ordered tuples, and we require that they be permutation invariant, which is equivalent to treating the ordered tuples as sets.
The $\rho$-sum Decomposition
It turns out that NPs were not the first model to bump into this question. The Neural Statistician4 and PointNet5, to name a few examples, also considered the problem of representing sets, and proposed a similar solution to CNPs. This form has been dubbed the sum decomposition, or Deep Sets. For a set $S$, it is computed as follows \begin{equation} \hat{f}(S) = \rho \left( \sum_{s \in S} \phi (s) \right),\tag{3}\label{eq3} \end{equation}
where $\phi$ maps elements of $s$ to a vector space $\mathbb{R}^D$, and $\rho$ maps elements of $\mathbb{R}^D$ to $\mathbb{R}^M$. Note that this is a more general form of the NP computational graph: in Fig. 3, $e$ plays the role of $\phi$, and $d$ is (a conditional version of) $\rho$. Here the pooling operation ($a$ in Fig. 3) is implemented as a sum. This was also the operation chosen in the Neural Statistician and PointNet. However, the role of this operation is to enforce the permutation invariance, and as such, any permutation invariant pooling operation (such as mean or max) would also be suitable in place of the sum.
Zaheer et al. (2017)6 provided a more rigorous treatment of the issue. Their key theorem demonstrates that (with a few important caveats) any function on sets has a representation of the form of Eq. $\eqref{eq3}$. Some caveats are that $\phi$ and $\rho$ must be universal approximators of their function class (motivating the use of neural networks). An important caveat is, when the elements of $S$ are drawn from an uncountable set (e.g., $\mathbb{R}^m$), the theorem was only proven for fixed-size sets. So just shy of the varying-sized requirement.
Despite the caveats on the theory, Zaheer et al. provide a remarkable result towards characterizing the approximation capabilities of Deep Set networks, and justify their usage in a wide range of machine learning problems.
Summary
I'll wrap this post up here. We have taken a first look at the Neural Process family of models for probabilistic meta-learning, and looked at the key role that representation learning on sets plays in this model class.
The modelling of sets is an important and fascinating subfield at the frontier of machine learning research. Personally, I am drawn to this topic due to its role in meta-learning, a topic which is near and dear to my heart. Indeed, one perspective on the meta / few-shot learning lines of research is as learning to flexibly condition models on small sets of data, and of course, Deep Sets have a key role to play in that perspective.
Neural processes take just this approach. Arguably the most straightforward perspective of CNPs is as simple predictors parameterized by DeepSet networks, trained with maximum-likelihood. Yet this simple approach has proven extremely powerful. The original papers show that this class of models is capable of providing strong probabilistic predictions, and perform very well in the low-data regime. Requeima et al.7 run with this view, and adapt CNPs to the large scale, few-shot image classification setting, demonstrating very strong performance (full disclosure, I am an author on that paper). Finally, there have been several recent advances in this class of models that has increased their capacity and addressed their susceptibility to underfitting. These will be the topic of my next post on Attentive8 and Convolutional9 CNPs.
References
-
Brendan Lake et al. Human-level concept learning through probabilistic program induction. 2015 ↩
-
Harrison Edwards and Amos Storkey. Towards a neural statistician. 2016 ↩
-
Charles R. Qi et al. PointNet: deep learning on point sets for 3d classification and segmentation. 2016 ↩
-
James Requeima et al. Fast and flexible multi-task classification with conditional neural adaptive processes. 2019 ↩
-
Jonathan Gordon et al. Convolutional Conditional Neural Processes. 2020 ↩


Allocate memory using the ArrayFire memory manager.
More...
|
| AFAPI af_err | af_alloc_device (void **ptr, const dim_t bytes) |
| | Allocates memory using ArrayFire's memory manager.
|
| |
| AFAPI af_err | af_alloc_device_v2 (void **ptr, const dim_t bytes) |
| | Allocates memory using ArrayFire's memory manager.
|
| |
| AFAPI void * | alloc (const size_t elements, const dtype type) |
| | Allocates memory using ArrayFire's memory manager.
|
| |
| AFAPI void * | allocV2 (const size_t bytes) |
| | Allocates memory using ArrayFire's memory manager.
|
| |
| template<typename T > |
| T * | alloc (const size_t elements) |
| | Allocates memory using ArrayFire's memory manager.
|
| |
Allocate memory using the ArrayFire memory manager.
This function will allocate memory on the device and return a pointer to it. The memory is allocated using ArrayFire's memory manager which will defer releasing memory to the driver and reuse the same memory for later operations.
This function will return different objects based on the type used. The interface returns a void pointer that needs to be cast to the backend appropriate memory type.
| function | CPU | CUDA | OpenCL |
| af_alloc_device_v2 | T* | T* | cl_mem |
| af::allocV2 | T* | T* | cl_mem |
| af_alloc_device (deprecated) | T* | T* | cl::Buffer* |
| af::alloc (deprecated) | T* | T* | cl::Buffer* |
CPU Backend
float *dptr = static_cast<float *>(ptr);
dptr[0] = 5.0f;
freeV2(ptr);
AFAPI void * allocV2(const size_t bytes)
Allocates memory using ArrayFire's memory manager.
CUDA Backend
void *ptr = allocV2(sizeof(float));
float *dptr = static_cast<float *>(ptr);
float host_data = 5.0f;
cudaError_t error = cudaSuccess;
error = cudaMemcpy(dptr, &host_data, sizeof(float), cudaMemcpyHostToDevice);
freeV2(ptr);
OpenCL Backend
cl_command_queue queue;
cl_context context;
void *alloc_ptr = allocV2(sizeof(float));
cl_mem mem = static_cast<cl_mem>(alloc_ptr);
cl_int map_err_code;
void *mapped_ptr = clEnqueueMapBuffer(
queue,
mem,
CL_TRUE,
CL_MAP_READ | CL_MAP_WRITE,
0,
sizeof(float),
0,
nullptr,
nullptr,
&map_err_code);
float *float_ptr = static_cast<float *>(mapped_ptr);
float_ptr[0] = 5.0f;
cl_int unmap_err_code =
clEnqueueUnmapMemObject(queue,
mem,
mapped_ptr,
0,
nullptr,
nullptr);
freeV2(alloc_ptr);
AFAPI af_err afcl_get_queue(cl_command_queue *queue, const bool retain)
Get a handle to ArrayFire's OpenCL command queue.
AFAPI af_err afcl_get_context(cl_context *ctx, const bool retain)
Get a handle to ArrayFire's OpenCL context.
◆ af_alloc_device()
Allocates memory using ArrayFire's memory manager.
This device memory returned by this function can only be freed using af_free_device
- Parameters
-
| [out] | ptr | Pointer to the device memory on the current device. This is a CUDA device pointer for the CUDA backend. A cl::Buffer pointer on the OpenCL backend and a C pointer for the CPU backend |
| [in] | bytes | The number of bites to allocate on the device |
- Returns
- AF_SUCCESS if a pointer could be allocated. AF_ERR_NO_MEM if there is no memory
- Deprecated:
- Use af_alloc_device_v2 instead. af_alloc_device_v2 returns a cl_mem object instead of the cl::Buffer object for the OpenCL backend. Otherwise the functionallity is identical
◆ af_alloc_device_v2()
Allocates memory using ArrayFire's memory manager.
This device memory returned by this function can only be freed using af_free_device_v2.
- Parameters
-
| [out] | ptr | Pointer to the device memory on the current device. This is a CUDA device pointer for the CUDA backend. A cl::Buffer pointer on the OpenCL backend and a C pointer for the CPU backend |
| [in] | bytes | The number of bites to allocate on the device |
- Returns
- AF_SUCCESS if a pointer could be allocated. AF_ERR_NO_MEM if there is no memory
◆ alloc() [1/2]
| T * alloc |
( |
const size_t |
elements | ) |
|
Allocates memory using ArrayFire's memory manager.
- Parameters
-
| [in] | elements | the number of elements to allocate |
- Returns
- Pointer to the device memory on the current device. This is a CUDA device pointer for the CUDA backend. A cl::Buffer pointer from the cl2.hpp header on the OpenCL backend and a C pointer for the CPU backend
- Note
- the size of the memory allocated is the number of
elements
* sizeof(type)
-
The device memory returned by this function is only freed if af::free() is called explicitly
- Deprecated:
- Use allocV2 instead. allocV2 accepts number of bytes instead of number of elements and returns a cl_mem object instead of the cl::Buffer object for the OpenCL backend. Otherwise the functionallity is identical to af::alloc.
◆ alloc() [2/2]
| AFAPI void * alloc |
( |
const size_t |
elements, |
|
|
const dtype |
type |
|
) |
| |
Allocates memory using ArrayFire's memory manager.
- Parameters
-
| [in] | elements | the number of elements to allocate |
| [in] | type | is the type of the elements to allocate |
- Returns
- Pointer to the device memory on the current device. This is a CUDA device pointer for the CUDA backend. A cl::Buffer pointer from the cl2.hpp header on the OpenCL backend and a C pointer for the CPU backend
- Note
- The device memory returned by this function is only freed if af::free() is called explicitly
- Deprecated:
- Use allocV2 instead. allocV2 accepts number of bytes instead of number of elements and returns a cl_mem object instead of the cl::Buffer object for the OpenCL backend. Otherwise the functionallity is identical to af::alloc.
◆ allocV2()
| AFAPI void * allocV2 |
( |
const size_t |
bytes | ) |
|
Allocates memory using ArrayFire's memory manager.
- Parameters
-
| [in] | bytes | the number of bytes to allocate |
- Returns
- Pointer to the device memory on the current device. This is a CUDA device pointer for the CUDA backend. A cl_mem pointer on the OpenCL backend and a C pointer for the CPU backend
- Note
- The device memory returned by this function is only freed if af::freeV2() is called explicitly


--- layout: default parent: "ui-elements" sub_parent: "maps" activePage: "google-maps" pageTitle: "google maps" plugins: ["prism", "google-map-apis"] ---
Mono provides a variety of Google Map components with a little customization that suits its design standards. For more information, please see the official Google Map Documentaion.


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---
sort_order: 49
tei_id: rdx_b5j2m.p.idm188588320
annotation_count: 0
images:
small-thumbnail: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p750p/mini-thumbnail/
json: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p750p/info/
full: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p750p/fs/
page: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p750p/single-page/
thumbnail: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p750p/thumbnail/
title: Page 49
number: 49
---
{% raw %}A RESPONSIBLE JOURNEY.{% endraw %}
{% raw %}45{% endraw %}
{% raw %}My mind once made up, I set earnestly about the{% endraw %}
{% raw %}needful preparations. They were few and easily{% endraw %}
{% raw %}made. A one-horse waggon, well-stocked with oats,{% endraw %}
{% raw %}meal, and bacon, for our own and the horse's sup¬{% endraw %}
{% raw %}port, was soon made ready. My pride was aroused{% endraw %}
{% raw %}in view of the importance of my responsibility, and{% endraw %}
{% raw %}heart and soul I became identified with my master's{% endraw %}
{% raw %}project of running off his negroes. The second{% endraw %}
{% raw %}night after the scheme was formed, we were under{% endraw %}
{% raw %}way. Fortunately for the success of the undertak¬{% endraw %}
{% raw %}ing, these people had long been under my direction,{% endraw %}
{% raw %}and were devotedly attached to me in return for the{% endraw %}
{% raw %}Tnany alleviations I had afforded to their miserable{% endraw %}
{% raw %}condition, the comforts I had procured them, and{% endraw %}
{% raw %}the consideration I had always manifested for them.{% endraw %}
{% raw %}Under these circumstances, no difficulty arose from{% endraw %}
{% raw %}want of submission to my authority. The dread of{% endraw %}
{% raw %}being separated, and sold away down south, should{% endraw %}
{% raw %}they remain on the old estate, united them as one{% endraw %}
{% raw %}man, and kept them patient and alert.{% endraw %}
{% raw %}We started from home about eleven o'clock at{% endraw %}
{% raw %}night, and till the following noon made no permanent{% endraw %}
{% raw %}halt. The men trudged on foot, the children were{% endraw %}
{% raw %}put into the waggon, and now and then my wife{% endraw %}
{% raw %}Tode for a while. On we went through Alexandria,{% endraw %}
{% raw %}Culpepper, Fauquier, Harper's Ferry, Cumberland,{% endraw %}
{% raw %}ever the mountains on the National Turnpike to{% endraw %}
{% raw %}Wheeling. In all the taverns along the road there{% endraw %}
{% raw %}were regular places for the droves of negroes who{% endraw %}
{% raw %}were continually passing through the country{% endraw %}
{% raw %}under the care of overseers. In these we lodged,{% endraw %}
{% raw %}■and our lodging constituted our only expense, for our{% endraw %}
{% raw %}p{% endraw %}


--- layout: default title: "404: Page not found" permalink: 404.html ---
Well look at you, you broke it. I hope you're happy.


What can I search for?
General Rules
All searches support sub-string matches and casing is ignored.
Type *
, all
, everything
or
anything
to get the entire catalog.
Item — Properties
ID
"Merchant2H_ATC"
Name
"Aegean Trader's Contract"
Rarity
"Legendary"
Type
"Merchant Gear"
Level
"Level 17"
Effect Name
"Snare Resist"
Crafting School
"Infantry School"
Crafting Material
"Arctic Fox Furs"
Vendor ID
"A02_Cap_BasicStore01"
Vendor Name
"Farafra General Store"
Vendor Location
"Farafra Oasis"
Item — Keywords
Group
weapon, armor, vanity
Crafting
recipe, craftable
Sold
buyable, purchaseable, shop, store, vendor, sold
Currency
coins, money, gold, points, tokens, coin, empire, sparta,
crete, halloween, winter, summer
Alliance
alliance, council of imhotep, delian league, legion of
carthage
Event
halloween, winter, reward, summer
Quest
quest, reward
Reforgeable
reforgeable
Legendary Rotation
classic, celeste, legendary rotation
Starting Gear
babylonian, persian, norse, starting gear
Loot table
crete, crete-exclusive, skirmish, skirmish-exclusive, bahram, bahram's lottery-exclusive, moe's mystorium exclusive
Advisor — Properties
ID
"Alexander_L_IV"
Name
"Alexander The Great"
Description
"Toxotai and Peltasts attack 25% faster"
Rarity
"Legendary"
Age
"Age 4"
Level
"Level 7"
Civilization
"Greek"
Vendor ID
"Mn_Cap_BasicStore03"
Vendor Name
"Minoan General Store"
Vendor Location
"Crete"
Set
"Celeste", "Classic"
Advisor — Keywords
Sold
buyable, purchaseable, shop, store, vendor, sold
Currency
coins, money, gold, points, tokens, coin, empire, sparta,
crete, halloween, winter, summer
Loot table
crete, crete-exclusive, skirmish, skirmish-exclusive
Blueprint — Properties
ID
"Van_Gazebo1"
Name
"Round Greek Gazebo"
Description
"24-slot Warehouse"
Rarity
"Rare"
Type
"Merchant Gear"
Crafting Material
"Olive Oil"
Vendor ID
"Gn_Cap_VanityGearStore02"
Vendor Name
"Heroic Vanity"
Vendor Location
"Vanity Island"
Blueprint — Keywords
Sold
buyable, purchaseable, shop, store, vendor, sold
Currency
coins, money, gold, points, tokens, coin, empire, sparta,
crete, halloween, winter, summer
Event
halloween, winter, reward, summer
Loot table
crete, crete-exclusive, skirmish, skirmish-exclusive
Recipe — Properties
ID
"ArmorPlt_R009"
Name
"Cold-Forged Armoring"
Description
"Use: Grants an Engineering School the ability to create this equipment"
Rarity
"Rare"
School
"Engineering School", "Tool Maker's Workshop"
Crafting Material
"Illuminated Codexes"
Vendor ID
"At_Cap_BasicStore01"
Vendor Name
"Athens Basic Store"
Vendor Location
"Athens"
Recipe — Keywords
Sold
buyable, purchaseable, shop, store, vendor, sold
Currency
coins, money, gold, points, tokens, coin, empire, sparta,
crete, halloween, winter, summer
Loot table
crete, crete-exclusive, skirmish, skirmish-exclusive
Materials — Properties
Name
Celtic Charcoal
Rarity
"Rare"
Materials — Keywords
Use
unusable
Event
halloween, winter, reward, summer
Quest
quest, reward
Loot table
crete, crete-exclusive
Workshop
logger, mason, skinner, smelter, farmer, gem cutter, toolmaker,
alchemist, weaver, scrivener


COVID-19 Everyday Prevention Actions
The Centers for Disease Control and Prevention is working to help keep you and your community safe from the threat of novel, or new coronavirus.
Take the following everyday steps to help avoid the spread of all respiratory viruses:
- Wash your hands often with soap and water for at least 20 seconds.
- Cover your cough or sneeze with a tissue, throw the tissue away, and then wash your hands.
- Avoid touching your eyes, nose, and mouth.
- Clean and disinfect frequently touched objects or surfaces such as remote controls and doorknobs.
- Avoid close contact with people who are sick.
- Stay home if you are sick. Call your doctor if you develop fever, cough, or difficulty breathing.
For more tips, visit cdc.gov.


It's a demo webpage that show how works FortressJS default view zone/page/view engine
Your ip is : _IP


Vol.1
The Biggest SNS connecting people
The Answer is:
Go on to make it!
Congratulations! You make it ALL!
Do look forward App Guessing Show Vol.2


Christopher Ellis
About
Portfolio
Contact
Portfolio
Portfolio-One
RPG Game
Trivia Game
Rutgers Info Widget
Rock, Paper, Scissors


Senior in the Geography Department pursuing the Data Science option with minors in Informatics and Applied Math.
Arghya is passionate about using geospatial technology to study and address environmental injustice and systemic inequity. In her free time, she likes to read, dance, and go on walks.
Senior in the Geography Department pursuing the Data Science option with a minor in Informatics.
Bukhari's primary interests lie in understanding data modeling methods that help transform the way we understand the spaces we inhabit . In his free time, he enjoys photography, spending time with friends, and playing tennis.
Senior at UW pursuing the GIS track in the Geography department.
Aditi is interested in how geospatial analysis plays a role in business and economic development. She is passionate about digital marketing in the cybersecurity industry. In her free time she enjoys writing, drawing, practicing taekwondo and volunteering.
Junior in the Geography Department pursuing the Data Science option.
Melissa is interested in GIS mapping, environmental sustainability, and patterns of human migration. In her free time she enjoys reading and baking. She is looking forward to seeing your submissions and guiding in your publication goals.
Senior at UW studying Geography and Philosophy.
Stella is especially interested in recontextualizing the ways we see world politics and social issues through a geographical lens. In her free time, she enjoys listening to music, playing games, and playing guitar.
Junior pursuing the Geography: Data Science Degree
Kaiji’s interests include using geodemographics and data visualization to better understand the relationships between populations and their geographic location. In his free time, Kaiji enjoys listening to music, playing video games, and making beats.
Senior at UW pursuing the Geography: Data Science Degree and Economics, with a minor in Informatics.
Kevin is interested in data analytics & visualization and enjoys discovering new meanings through data. He also particularly likes playing basketball, scuba dive, and spend time with friends.


You're looking at the Spiffy UI unit tests. They help us make sure the framework keeps running well and we aren't breaking any of our APIs.
These tests are running in your browser right now. If you refresh the page they will run again. You can access this page using any browser and the tests run automatically.
The Spiffy UI unit tests were written in GWT and use the JQuery/QUnit testing framework.
Return to the Spiffy UI framework.


Dropped files will stay within your local browser. They will not be uploaded anywhere else.
| Name | {{ upload.file.name }} |
| File type | {{ upload.file.type }} |
| Size | {{ upload.file.size | bytes}} |
| Last modified | {{ upload.file.lastModifiedDate }} |
Failed to parse {{ upload.file.name }}.
Error message: {{ upload.parseError }}.
If you believe this is a bug, please open an
issue here.
Don't forget to attach the audio file which could not be parsed.
| Format | {{ picture.format }} |
| Type | {{ picture.type }} |
| Description | {{ picture.description }} |
| Label | Value | Property name |
|---|---|---|
| {{tag.label.text}}{{tag.label.text}} | , {{v.text}} {{v.text}} | {{tag.key}} |
| Key | Value |
|---|---|
| {{tag.id }} | {{ tag.value }} |


Plea in Supreme Court to stall Sena-NCP-Congress government in Maharashtra
Alliance will defeat electoral mandate’
by Legal CorrespondentA Mumbai resident on Friday moved a petition in the Supreme Court seeking to stop the “unholy alliance” between the NCP, Congress and Shiv Sena to form the government in Maharashtra.
Surendra Indrabahadur Singh, represented by advocate Sriram Parakkat, said these three parties who had “fought against each other is trying to form government by joining hands thereby defeating the electoral mandate”.
The petition asked the apex court to consider the meaning and purport of the term “largest party/group” in the context of the Governor’s power to invite the elected party to form government under Article 164 of the Constitution.
The petition asked the court to clarify on certain questions of law. “Does the expression include a group of parties that contested against each other ...Whether an alliance between two political parties that contested against each other is acceptable under present constitutional scheme...”


A new dataset is avaliable here:
https://openfmri.org{{ url }}
Project Name: {{ dataset.project_name }}
Summary:
{{ dataset.summary|safe }}


Usage
- Create div inside
div.slides
- Each div is your slide
Navigation
- ➡ or
Space
to advance to the next slide
- ⬅ go back to previous slide
Goto slide
g
+ slide number (1-based, first slide is slide 1) to go to that slide
- (If the slide doesn't exist, or the input is not valid, nothing will happen)


Click here to register an account and start writing blogs!
Click here to request a password reset token so you can change your password.


Flame Successor Akkan:
My older sister Cardia told me long ago that she wasn't fit to be queen.
Now I know why! I'll never forget what I felt during those fights! I'm not sure what's coming next, but at least I know myself!
Take this Stone of Commune, you'll need it on your pilgrimage. Now I must return to Flame Lord Kakai. Goodbye for now!
"Wait!"


This is the left panel.
Close
Test Panel
Open Left Panel!
Open Right Panel!
This is the right panel.
Close


| Class | Description |
|---|---|
| ArrayOfBooleansSerDe |
Methods of serializing and deserializing arrays of Boolean as a bit array.
|
| ArrayOfDoublesSerDe |
Methods of serializing and deserializing arrays of Double.
|
| ArrayOfItemsSerDe<T> |
Base class for serializing and deserializing custom types.
|
| ArrayOfLongsSerDe |
Methods of serializing and deserializing arrays of Long.
|
| ArrayOfNumbersSerDe |
Methods of serializing and deserializing arrays of the object version of primitive types of
Number.
|
| ArrayOfStringsSerDe |
Methods of serializing and deserializing arrays of String.
|
| ArrayOfUtf16StringsSerDe |
Methods of serializing and deserializing arrays of String.
|
| BinarySearch |
Contains common equality binary search algorithms.
|
| BinomialBoundsN |
This class enables the estimation of error bounds given a sample set size, the sampling
probability theta, the number of standard deviations and a simple noDataSeen flag.
|
| BoundsOnBinomialProportions |
Confidence intervals for binomial proportions.
|
| BoundsOnRatiosInSampledSets |
This class is used to compute the bounds on the estimate of the ratio |B| / |A|, where:
|A| is the unknown size of a set A of unique identifiers.
|B| is the unknown size of a subset B of A.
a = |SA| is the observed size of a sample of A
that was obtained by Bernoulli sampling with a known inclusion probability f.
b = |SA ∩ B| is the observed size of a subset
of SA.
|
| BoundsOnRatiosInThetaSketchedSets |
This class is used to compute the bounds on the estimate of the ratio B / A, where:
A is a Theta Sketch of population PopA.
B is a Theta Sketch of population PopB that is a subset of A,
obtained by an intersection of A with some other Theta Sketch C,
which acts like a predicate or selection clause.
The estimate of the ratio PopB/PopA is
BoundsOnRatiosInThetaSketchedSets.getEstimateOfBoverA(A, B).
The Upper Bound estimate on the ratio PopB/PopA is
BoundsOnRatiosInThetaSketchedSets.getUpperBoundForBoverA(A, B).
The Lower Bound estimate on the ratio PopB/PopA is
BoundsOnRatiosInThetaSketchedSets.getLowerBoundForBoverA(A, B).
Note: The theta of A cannot be greater than the theta of B.
|
| BoundsOnRatiosInTupleSketchedSets |
This class is used to compute the bounds on the estimate of the ratio B / A, where:
A is a Tuple Sketch of population PopA.
B is a Tuple or Theta Sketch of population PopB that is a subset of A,
obtained by an intersection of A with some other Tuple or Theta Sketch C,
which acts like a predicate or selection clause.
The estimate of the ratio PopB/PopA is
BoundsOnRatiosInThetaSketchedSets.getEstimateOfBoverA(A, B).
The Upper Bound estimate on the ratio PopB/PopA is
BoundsOnRatiosInThetaSketchedSets.getUpperBoundForBoverA(A, B).
The Lower Bound estimate on the ratio PopB/PopA is
BoundsOnRatiosInThetaSketchedSets.getLowerBoundForBoverA(A, B).
Note: The theta of A cannot be greater than the theta of B.
|
| ByteArrayUtil |
Useful methods for byte arrays.
|
| GenericInequalitySearch |
This provides efficient, unique and unambiguous binary searching for inequalities
for ordered arrays of values that may include duplicate values.
|
| HashOperations |
Helper class for the common hash table methods.
|
| QuantilesHelper |
Common static methods for quantiles sketches
|
| QuickSelect |
QuickSelect algorithm improved from Sedgewick.
|
| Util |
Common utility functions.
|
| Enum | Description |
|---|---|
| Family |
Defines the various families of sketch and set operation classes.
|
| GenericInequalitySearch.Inequality |
The enumerator of inequalities
|
| InequalitySearch |
This provides efficient, unique and unambiguous binary searching for inequality comparison criteria
for ordered arrays of values that may include duplicate values.
|
| ResizeFactor |
For the Families that accept this configuration parameter, it controls the size multiple that
affects how fast the internal cache grows, when more space is required.
|
| Exception | Description |
|---|---|
| SketchesArgumentException |
Illegal Arguments Exception class for the library
|
| SketchesException |
Exception class for the library
|
| SketchesReadOnlyException |
Write operation attempted on a read-only class.
|
| SketchesStateException |
Illegal State Exception class for the library
|
Copyright © 2015–2021 The Apache Software Foundation. All rights reserved.


WRONGFULLY ACCUSED A film review by Matt Williams
RATING: * out of * * * *
Talk about beating a dead horse. Leslie Nielsen is still trying to squeeze the last drop of humor out of the spoof comedy. Give up! It's bone-dry, as his latest comedic train wreck, Wrongfully Accused, can attest.
The film roughly follows the plot of The Fugitive, but spoofs a variety of films in-between, from The Empire Strikes Back to Titanic.  He's falsely accused of murder and must escape to find the real killer: a one armed, one legged, one eyed man (Aaron Pearl).
In the Tommy Lee Jones role here is Richard Crenna as Fergus Falls, the U.S. Marshal assigned to track Harrison down. You'd think that the filmmakers would be able to get some mileage out of Tommy Lee Jones' Oscar-winning role. No such luck. Crenna barks out orders in Jones-style, but his character is so uninteresting that he goes missing for most of the film's last third, and you never notice the difference.
And of course the film has to have some sort of love interest. That job is dually provided by Kelly LeBrock and Melinda McGraw. One of these women has Ryan's best interests at heart...the other wants to kill him. But he doesn't know which is which, and the audience couldn't care less.
The film is packed with wall-to-wall non sequitur gags, but the film's escape sequence is the only one which even mildly works, and it is mostly successful only in comparison to the rest of the film.
One of the big problems with the humor here is the source material. Rather than spoofing a genre, Wrongfully Accused decided to spoof one specific film, The Fugitive. As all the comedic potential is tapped early, the film grasps out for other sources of inspiration. However, the amalgamated hodgepodge of scenes are so desperately humorless that it seems the filmmakers are simply playing a game of "Guess Which Film We're Spoofing Now!"
There have been worse spoof comedys...but not many. For the crime of being unfunny, Wrongfully Accused is guilty, guilty, guilty.
Copyright 1998 Matt Williams
- Matt Williams<EMAIL_ADDRESS>Reviewer for Cinematter: http://www.cinematter.com Home of over 600 reviews, and information on nearly 700 upcoming releases
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


--- layout: page title: "Constrained RESTful Environments (CoRE) Link Format" description: "This specification defines Web Linking using a link format for use by constrained web servers to describe hosted resources, their attributes, and other relationships between links. Based on the HTTP Link Header field defined in RFC 5988, the Constrained RESTful Environments (CoRE) Link Format is carried as a payload and is assigned an Internet media type. \"RESTful\" refers to the Representational State Transfer (REST) architecture. A well-known URI is defined as a default entry point for requesting the links hosted by a server." ---
| Document Name: | RFC 6690 |
|---|---|
| Document URI: |
urn:ietf:rfc:6690
|
| Online Version: |
https://datatracker.ietf.org/doc/html/rfc6690
|
| Organization: | Internet Engineering Task Force (IETF) |
| Series: | Request for Comments (RFC) |
| Abstract: | This specification defines Web Linking using a link format for use by constrained web servers to describe hosted resources, their attributes, and other relationships between links. Based on the HTTP Link Header field defined in RFC 5988, the Constrained RESTful Environments (CoRE) Link Format is carried as a payload and is assigned an Internet media type. "RESTful" refers to the Representational State Transfer (REST) architecture. A well-known URI is defined as a default entry point for requesting the links hosted by a server. |
hosts
application/link-format
core
Return to ( Series | Organization | all Specifications )


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{% extends '_layouts/layout_2-3_no_title_block.html' %} {% block page_title %} Stomach ache symptoms and conditions - NHS.UK {% endblock %} {% block pageTitle %} Is this your surgery? {% endblock %} {% block content %}
Click on the GP surgery.
If this isn't your surgery, check that you're using the right name and search again.
{% endblock %} {% block emergencyContent %} {% endblock%} {% block mutedCallout%} {% endblock%} {% block body_after %} {% endblock %}


The Last Days of Disco
The Last Days of Disco is the middle part of Whit Stillmans yuppie trilogy, bookended by Metropolitan and Barcelona. Though it is chronologically the earliest of the three, Disco shows what happens to the characters in Metropolitan once they get out of college, and it anticipates the Spanish setting of Barcelona. None of the three movies have characters in common, of course, but thats just a technicality.
Whit Stillmans films are all of a piece. The characters are generically the same brainy, well-off Eastern kids living in the big city, inhabiting a scene, making witty dialog. Its like Friends with a brain. Dialog is key, and always wonderfully written.  As such Stillman is an acquired taste. I like him, but I wouldnt force him on anyone else.
The time is the very early 80s. Disco covers six months in the lives of a handful of post-college kids living and working in New York. Two work in publishing, one in advertising, another is an assistant District Attorney, and the last is a manager at a club based loosely on the old Studio 54. They all have a history dating back years or at least months, and they all hang around the club, which is run by a tax-evading scumbag. They work during the day, go clubbing at night, share apartments, have sex and talk and talk and talk.
Now I always like two things about a Stillman film. I like the crisp, pseudo-intellectual dialog (a deconstruction of Lady and the Tramp is hilarious) and I like the characters themselves, their relations with each other, the way they insult each other and fall in and out of love with each other and bother each other until whatever it was holding them together falls apart and they all go their separate ways with a feeling of regret. I shouldnt like them, because they are all rich (or at least come from affluent families) and somewhat snobbish and they all have certain expectations out of life and are at least a little resentful of outsiders. Stillman was one of them, by the way, and thats probably why hes able to portray rich kids so sympathetically. He doesnt take them altogether seriously, of course, but neither does he treat them like cardboard cutouts in a class warfare morality play. Stillman allows his characters all the dignity that they deserve, which is not to say that he finds them particularly admirable. Theyre just people.
The ending is no surprise, of course; everyone knows disco died, and these people felt it. Felt it hard, actually. They like disco and the ending explains a little why some people might actually find such a thing valuable. It was a stage in their lives and when it ended they all moved on, to another scene or another stage, with, as I said, regret, and even a little wisdom. Life has a way of happening like that. Highly recommended, if you liked Metropolitan.
Kevin W. Welch<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


{% include head.html %}
{{ site.title | default: site.github.repository_name }}
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BitPoet
Make some magic. Make a poem.
Select specific text if you're feeling picky.
Or go wild and use the whole website.
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Integrated Clinical Platform
John Paul K,
CRA
My Activities
My Monitoring
Project Governance
Complete documentation–High-23-10-2012
Complete coder queries–High-10-05-2012
Submit Sampling data–Low–18-04-2011
eTMF
Calendar
2
Overdue Visits
4
Overcome Visits
Visit Reports
CRA Plan
Budget
Observational
Trials
Enrollment
Site Monitoring
Trial Dashboard
Subject Visit Tracking
Risk Monitoring
CRF Query
Document
Tracking
Deviations


Bottle is a micro framework designed for prototyping and building small web applications and services. It stays out of your way and allows you to get things done fast, but misses some advanced features and ready-to-use solutions found in other frameworks (MVC, ORM, form validation, scaffolding, XML-RPC). Although it is possible to add these features and build complex applications with Bottle, you should consider using a full-stack Web framework like pylons or paste instead.
Bottle searches in ./ and ./views/ for templates. In a mod_python or mod_wsgi environment, the working directory (./) depends on your Apache settings. You should add an absolute path to the template search path:
bottle.TEMPLATE_PATH.insert(0,'/absolut/path/to/templates/')
so bottle searches the right paths.
In dynamic route syntax, a placeholder token (:name) matches everything up to the next slash. This equals to [^/]+ in regular expression syntax. To accept slashes too, you have to add a custom regular pattern to the placeholder. An example: /images/:filepath#.*# would match /images/icons/error.png but /images/:filename won’t.
Redirects and url-building only works if bottle knows the public address and location of your application. If you run bottle locally behind a reverse proxy or load balancer, some information might get lost along the way. For example, the wsgi.url_scheme value or the Host header might reflect the local request by your proxy, not the real request by the client. Here is a small WSGI middleware snippet that helps to fix these values:
def fix_environ_middleware(app):
def fixed_app(environ, start_response):
environ['wsgi.url_scheme'] = 'https'
environ['HTTP_X_FORWARDED_HOST'] = 'example.com'
return app(environ, start_response)
return https_app
app = bottle.default_app()
app.wsgi = fix_environ_middleware(app.wsgi)


Dealing with conflict
The best examples of dealing with conflict are probably customer service based as I have been in this industry going on 7 years now. To talk about conflict I will use an example of a dissatisfied customer;
realised until the food came out of the kitchen that it was incorrect.
Now it also happened that this table had been waiting too long (in their eyes and mine) for their food to come out, and so when the order was wrong as well this created a lot of frustration on their half. On my side of things it was a double faux par-the wait for food is bad enough, especially when that is down to the kitchens responsibility in timing, but in the hospitality industry this always directly reflects on the server rather than the kitchen when it comes to complaining.
Handling conflicts such as this during service might be very different to how one might react to conflict with friends or family. You need to make it obvious that it is your fault(even if it isn’t), apologise profusely and in many cases offer an exchange/complimentary item to the customer.
The customer was now just stating over and over again that it had taken too long and they had no time to wait for the correct order to be cooked, I felt in this case I had offered everything within my power but they were still dissatisfied and wouldn’t take any complimentary items to make up for it. My ultimate way of dealing with the situation was to ask if I could do anything else to make this better (which was curtly replied to with a no), apologise again and then to leave the situation.
The source of this conflict was an action of mine, albeit not an intentional action. I always try to remember this when it seems a customer(or anyone else) is reacting as though you have made the mistake on purpose, as if you wanted to annoy them.
I would describe my actions/reactions to the unhappy customer as calm, clear and aiming to be effective; this doesn’t mean that my actions could ever have made the conflict better, but they probably didn’t make it worse either.
In these situations is really does depend on the customer you are dealing with and if you have built up a rapport with them since they have walked in. Building rapport beforehand may make it easier to deal with any conflict that might possibly arise later on, you can apply this to many situations, such as working with other people, once you have an understanding of each other and what each other might appreciate or not it can be easier to make up for any mistakes made on your part.
 Each customer behaves differently due to personality, but you also don’t know whats going on in their lives right now; yeah you might feel personally attacked by their reaction but nearly all of the time is not the case, they might have been having a really bad day/week/month etc, they may have been given some bad news or be in a rush to get somewhere, or then again they might just be high maintenance people!!
From this experience and many others I have learnt that just because someone else is reacting unfairly or stubbornly, retaliating in that way will only lengthen the situation and draw out any conflict; keeping calm and stating facts rather than opinions also helps to keep the line of objective vs subjective from blurring.
Clear communication about how the situation came about can also help diffuse any disagreements if it is becoming opinionated and heated, for a customer it is better for them to hear “I’m sorry about xyz, it’s completely my fault because xyz, but I can offer xyz to you as an apology” than you trying to argue that its not your fault and you can’t do anything to make it better. Laziness sometimes leads to the latter, you can’t be bothered to go out of your way to check with your manager if you can give away a free coffee or take something off the bill for a customer, this makes the customer feel that you don’t care about resolving the conflict which is almost the opposite to a servers job description!


NOWHERE TO RUN A film review by James Berardinelli Copyright 1993 James Berardinelli
Running Length: 1:34 Rated: R (Violence, language, nudity)
Starring: Jean-Claude Van Damme, Rosanna Arquette, Kieran Culkin, Ted Levine, Joss Ackland Director: Robert Harmon Producers: Craig Baumgarten and Gary Adelson Screenplay: Joe Eszterhas, Leslie Bohem, and Randy Feldman Music: Mark Isham Released by Columbia Pictures
Jean-Claude Van Damme plays Sam, an escaped convict on the run from the law who takes refuge on the land of a pretty, debt-stricken widow by the name of Clydie (Rosanna Arquette). Clydie's son Mookie (Kieran Culkin) discovers Sam and a tentative friendship begins. Meanwhile, a land developer (Joss Ackland) and his henchman (Ted Levine) plot to drive Clydie off her land so that they can use it as part of their new project. When magnanimous offers to buy the land fail, the pair resort to terror, but Sam's presence creates some unexpected complications.
I suppose I should give NOWHERE TO RUN credit in a couple of areas. In the first place, it legitimately attempts to develop the characters. There are numerous scenes whose entire purpose is to build the various relation- ships that form the linchpin of the film: Sam and Clydie; Sam, Mookie, and Bre (Mookie's sister); and Mookie, Bre, Clydie. Unfortunately, few of these scenes work, mainly because of a too-obvious reliance upon formulas, not only for setup, but dialogue as well. There are a lot of cliches floating around in NOWHERE TO RUN, and every facet of the storyline is predictable.
Secondly, the film takes a little chance in giving Van Damme's character a less-than-sterling background. He's a convicted criminal. This is an unusual move for a picture of this type, so, to minimize its impact, the script makes sure that the viewer realizes what a nice guy Sam is. Sure, he may have made a mistake in the past, but underneath, he has a heart of gold.
Finally, there's the ending, which, at least in part, is refreshingly contrary to expectations. That's not to say that it's a tremendously original finale, but at least something about it is a little different.
Surprisingly, there isn't much action in NOWHERE TO RUN, at least in comparison to Van Damme's previous films, and this is one of the reasons why the movie misses its mark. Except for a few short bursts here and there and one extended scene during the climax, the film is mostly talk. And since Van Damme's range as a dramatic actor is marginal at best, this is a problem.
It's apparent that NOWHERE TO RUN's best asset is Rosanna Arquette, who puts spirit and energy into a fetching portrayal of Clydie. Not only is she pleasing to the eyes (a common trait of women in action films), but she can act circles around anyone else in the cast, except perhaps Joss Ackland, but he's on hand just to pick up a paycheck. Ms. Arquette saves more than her share of scenes. Entirely due to her performance, there's a flicker of chemistry between the two main characters.
The most serious flaw of NOWHERE TO RUN is that in trying to do too much, it succeeds at too little. Action fans will be disappointed by the amount of talk and the lack of fights. When there is action, nothing new, spectacular, or inventive is tried. Drama lovers (few of whom are likely to venture into a theater showing this film due to the reputation of its star) will have a hard time swallowing all the banalities. At least those with allegiance to Arquette and Van Damme will enjoy themselves. Both performers get an opportunity to take off their clothes (predictably, Van Damme's nude scene is less revealing).
NOWHERE TO RUN isn't a complete waste of time, nor is it terminally boring, but, as action/adventure pictures go, this one is closer to the bottom end of the scale than the top.
Rating: 6.1 (C, **)
- James Berardinelli<EMAIL_ADDRESS>.
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