Ploror688.github.io
Find out about Creative Writing and English Literature at Bath Spa University, including entry requirements, fees and how to apply. Creative Writing/English Literature BA (Hons)
Dear Twitpic Community - thank you for all the wonderful photos you have taken over the years. We have now placed Twitpic in an archived state. Research. Bath Spa University Research Centre in Creative Writing is a prospering centre of excellence, representing perhaps the strongest academic teams of creative. English literature and creative writing
Anna Davis Anna is the founder and Director of the Curtis Brown Creative writing school. She is the author of five novels, published around the world in 20 languages:.
Creative Writing and English Literature
Bath Spa University - Wikipedia. Bath Spa Creative Writing Staff — English literature. For aspiring writers, the Diploma offers involvement in a wide range of literary projects over two years of focused part-time study.
Develop your specialism. Spa offer places on the writing on the basis of your portfolio of creative writing and spa interview, which for overseas applicants Bath spa english literature and creative writing - mesin.org.
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Bath Spa University is a public university in Bath, England, with its main campus based at Newton Park on Duchy of Cornwall land. The University has other campuses.
Find more information about Creative Writing/English Literature BA (Hons) course at Bath Spa University, including course fees, module information and entry requirements.
Passionate about English Literature? . English Literature and Creative Writing graduate. . English Literature Literary Festivals.Hundreds of annual Book Festival. EzineArticles.com allows expert authors in hundreds of niche fields to get massive levels of exposure in exchange for the submission of their quality original articles. Published authors teach our creative writing courses. Bath Spa Creative Writing Staff – English literature. Welcome! Established online since 2008 we provide details of 350+ Community Literary Festivals throughout UK Ireland and beyond. We provide a literary news stream. Bath spa university creative writing – Simplicity. BA (Hons) English Literature – Bath Spa University. EzineArticles Submission - Submit Your Best Quality.
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This topic shows how some of the DITA elements look in the xhtml output when viewed in a browser. Some styling is default but additional CSS styling can create attractive effects.
Inline elements are used within a block of text.
| DITA element | xhtml element generated by OT | default styling | customised |
|---|---|---|---|
| codeph | samp (sample output) with class codeph | This is codeph inline styling with class codeph | |
| varname | var with class varname | This is varname inline styling with class varname | |
| parmname | span with class parmname | This is parmname inline styling with class parmname | |
| term | dfn with class term | This is term inline styling with class term. However, the element is italic by default and the class is not defined. | It is possible to style this differently with class term. |
| keyword | span with class keyword | The class keyword is not defined so no default styling | This is keyword inline styling with class keyword defined in custom CSS. |
| uicontrol | span with class uicontrol | This is uicontrol inline styling with span element with class uicontrol | This is uicontrol element with outputclass attribute keyboard added manually. Used to indicate Ctrl, Enter, Alt, etc. |
| filepath | span with class filepath | This is filepath inline c:\My Documents\Projects styling with span element with class filepath | |
| wintitle | span with class wintitle | This is wintitle inline styling with class wintitle | |
| ph | span without class | This is ph inline styling with span element but no class (so no default styling) | This is ph inline element with outputclass attribute italic added manually |
| menucascade | span with class menucascade | ||
| i | em | This is i inline styling output as em element so no class | |
| b | strong | This is b inline styling output as strong element so no class | |
| tt | tt | This is tt inline styling output as tt element so no class | |
| sup | sup | This is 205m3 styling output as sup element so no class | |
| sub | sub | This is H20 styling output as sub element so no class | |
| pre | pre |
The line breaks of preformatted text are maintained in the output. |
|
| codeblock | pre |
The element codeblock is also output as a pre element. |
This is a short list of interesting block elements. Most block elements cannot be inserted in a table so check out the other pages of this documentation to see others.
| DITA element | xhtml element generated by OT | sample xhtml output with default styling | xhtml output with customised styling |
|---|---|---|---|
| pre | pre |
The line breaks of preformatted text are maintained in the output. |
|
| codeblock | pre |
The element codeblock is also output as a pre element. |


{% extends "_email_base.html" %} {% block content %}
*|first_name|*,
Our meetings aren’t like the ones you go to at work. No boring powerpoints with a monotoned person droning on and on. We promise we’re not like that!
We just announced a new meeting! Take a look at the link and join us.
We look forward to seeing you.
The We All Code Team


ktor-server-core / io.ktor.routing / PathSegmentTailcardRouteSelector / <init>
<init>
PathSegmentTailcardRouteSelector(name: String = "")
Evaluates a route against any number of trailing path segments, and captures their values
Parameters
name
- is the name of the parameter to capture values to


{% extends 'base.html' %} {% block css %} {% endblock %} {% block content %}
On the left, there are many images. Please write a paragraph describing the biases in the image dataset.


CHOCOLAT
Reviewed by Harvey Karten Miramax Films Director: Lasse Hallstrom Writer: Joanne Harris (Novel) Robert Nelson Jacobs Cast: Juliette Binoche, Victoire Thivisol, Johnny Depp, Alfred Molina, Hugh O'Conor,Lena Olin, Peter Stormare, Judi Dench, Carrie-Anne Moss
Although chocolate is a high-calorie food, health honchos have discovered that it has benefits aside from its heavenly taste. Chocolate releases hormones that affect our mood and our feelings of love--but then we guys who have been giving boxes of the stuff to our women friends along with the flowers always knew that. Chocolate in moderate quantities has been found not to elevate cholesterol and some researchers even believe that it has anti-carcinogenic properties. No wonder, then, that this enchanted confection can change the attitudes of an entire town, as we see in a wonderfully atmospheric, exquisitely paced fable called "Chocolat." Though the principal performer is French and all action takes place in an isolated French village, the dialogue is English--which should be a selling point for the ninety-nine percent of American moviegoers who seem to eschew any film that requires the reading of subtitles.
As Roger Pratt's camera brings us to another world, the fictional village of Lansquenet that would be the delight of tourists but would bore the breeches off any urban traveler in two days, we wonder whether we are in the year 1363 rather than the actual time of the setting eighteen years after World War II. The cobblestone streets that embrace the shadow of the town's single church form the pavement for a group of citizens who appear alike in their beliefs but whose experiences run the gamut from a crotchety grandmother to an abused wife to an uptight mayor and a sex-starved matron.
As the entire hamlet pays homage to God on one blustery Sunday, in walks unwed mother Vianne Rocher (Juliette Binoche) together with her young daughter, Anouk (Victoire Thivisol). She is introduced to us as a wanderer whose agenda may be more than the mere opening of a chocolate shop. Renting a storefront from the grouchy old Armande Voizin (Judi Dench), she tries to coax the townspeople into patronage by offering free samples of her wares, but those not biting include the reactionary mayor, the Comte de Reynaud (Alfred Molina)--whose ancestor once chased the "radical" Huguenots out of the town and whose statue frowns down on the citizenry--and Josephine Muscat (Lena Olin), who is more interested in shoplifting a small box of goodies than in honest patronage.
Though the mayor, allied with the reluctant priest Pere Henri (Hugh O'Conor), urge a boycott of the store since it has opened during the Lenten season, most of the folks cannot resist taking in the aromas of the divine food. Vianne even wins over the kleptomaniac Josephine, who has been abused by her boorish husband Serge (Peter Stormare), and becomes an assistant in the shop regularly turning out Venus Nipples and an array of confections that would make New York's Godiva fans drool. When Vianne--whose refuses to go to church like the rest of the town--begins hanging out with the knavish Irish gypsy Roux (Johnny Depp), the lines are firmly drawn. Townspeople like up behind their mayor in favor of ridding the hamlet of both Vianne and the gypsy band, while others like Josephine and the newly cheerful grandmother, ally themselves with the newcomer.
Lasse Hallstrom's film, based on an adaptation of Joanne Harris's novel by Robert Nelson Jacobs, shares a motif with Gary Ross's "Pleasantville" (a brother and sister infiltrate a narrow-minded environs with their cosmopolitan sensibilities), with "Gabriel Axel's "Babette's Feast" (about people using religion as a substitute for life who are at least temporarily converted to worldliness via a gourmet dinner), and with Stanley Tucci's "Big Night" (immigrants trying to survive as restaurateurs against the competition of an established food emporium). "Chocolat," which is as light and fluffy as a bonbon, does not quite support the weight of its message-- that people everywhere must learn to tolerate those who are different from them--though the picture does most adequately convey the notion that we should all lighten up. One movie buff in effect mentions on an Internet site that the religious townspeople are made to look like ignorant hillbillies while the agnostic, nature-worshipping Vianne is the bearer of truth: that the film condescends, taking sides with the sophisticated wanderer who seems to need no money (she hands out more samples than she sells). This criticism has merit, just as does the similar point made about "Pleasantville." On the whole, "Chocolat" must be accepted as a fairy tale, beckoning the audience to suspend disbelief and to go with the central idea that conventional views ought to be challenged, that change is the nature of things, that life is not a dress rehearsal. "Chocolat" is a lovely, atmosphere-rich tale, a heartfelt, Capra-esque fable well suited to the coming holiday season.
Rated PG-13. Running time: 121 minutes. (C) 2000 by Harvey Karten<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


{% extends 'ui/page.html' %} {% load webcamp markdown_deux_tags dashboard %} {% block title %}Profile{% endblock %} {% block head %} {% endblock %} {% block content %}
| Name | {{ user.get_full_name }} |
|---|---|
| {{ user.email }} | |
| T-shirt size | {{ user.tshirt_size }} |
| {% icon "brands/github" %} Github | {% if user.github %} https://github.com/{{ user.github }} {% else %} - {% endif %} |
| {% icon "brands/twitter" %} Twitter | {% if user.twitter %} https://twitter.com/{{ user.twitter }} {% else %} - {% endif %} |
{{ application.about }}
{% if application.has_talk and application.talk.surveyscore %} {% with score=application.talk.surveyscore %}Here you can se what users thought of your talk. Be warned, the feedback was anonymous and the users can be harsh. The data is based on our exit survey where users could rate each talk they attended 1 to 5.
You don't have any submitted talks.
{% endfor %} {% if cfp.is_active %} {% endif %}This is a preview of your speaker profile, as will appear online if a talk of yours is accepted. Make it awesome.
{{ applicant.about }}
{{ applicant.biography|markdown }} {% include "talks/partials/speaker-links.html" %}You don't have a speaker profile yet. One will be created when you submit your first talk.
Create profile {% endif %} {% endwith %} {% endblock %}


Perf Report:
Test (Frame #10)
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Hi there! 😃
This is Lookie Lookie.
Please enable your webcam.
Training examples
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Start Training
Reset Model
Draw Heatmap
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Everything is at your finger tips! No longer worry about finding where to input your data or having to collect data. We do it for you! Just add, view or search for what you need and you're all set!
Our mission was to create an application that was simple, easy, and intuitive to use. We focused on simplicity and eliminated all other distractions in order to keep foucs on what really matters--the agricultural data.
"Where are the papers with all the data?", "I need a pen!", "I need to find the paper that says this on it?" Sounds familiar? Thankfully, you can now say goodbye to this problem!


Priest Gremory:
Nice! You got away clean, did you? Well, let's test the water.
Alas! The water fails the test. They must keep the real Grail somewhere else!
Well, thank you, I suppose. It was worth knowing about the false lead. Now leave me; I must return to my task of finding the real Grail. Go quickly, before they discover us!
"That wasn't the deal. You promised me a part of the Scripture of Monk in exchange for what we found."


Click here to download this week's dataset as a CSV
Click here to download this week's dataset as a Hyper Extract
Data Storytelling
There were several 'slicers' that could be applied to this data set - Prize, gender, age, country of birth, etc. I used these to show the data, with a set of BANs (Big A** Numbers) at the top of the viz to drive interactivity. Clicking on one of the Prizes filters the other vizzes on the dashboard to show only the pertinent data.
Data Viz Approach
Pretty straightforward this week. A chloropleth map to show the number of Laureates by birth country. This viz has a viz in tooltip to show the number of Laureates by Prize for the specified country. The majority of the remaining vizzes (with the exceptions of the BANs) were all bar graphs, with color indicating the Prize type. Though I loathe them, I thought a crosstab was a nice touch to be able to show the details of the Laureates.
Calculated Fields
Just a few this week. A couple to stitch some text together for tooltips, and the third to calculate the number of Laureates regardless of Prize. This was a Level of Detail calc that was used for the map.


Only the best come through the House.  Remember, we all must respect this House & do everything to protect'it.
Respect is Earned, Never Given, & Integrity is what this Home is built on. Oh yeah... & take yo mutha*uckin shoes off!


Openable Instances
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How to Choose the Right Cloud Deployment Model for your Application?
Understand and analyze different Cloud Deployment Models: public, private, hybrid and community. Select the perfect Cloud Deployment Models based on your App Architecture: VMs vs IaaS vs Containers vs Serverless. Read more to find out their performance & potential use cases.
When companies talk about moving to the cloud, it is a general assumption that they are bringing their on-premise workload to the public cloud and not switching clouds. However, GitLab, a startup famous for providing developers tools, announced earlier this year that they are abandoning Microsoft Azure and moving to Google Cloud Platform, as mentioned in the official GitLab statement.
This step took place because GitLab wanted to adopt cloud-native practices with the best use of microservices and containers. The same approach used by GitLab is becoming a critical factor for modern software development. Also, Kubernetes turned out their preferable choice since it allows elastic scaling from a couple of users to millions.
GitLab is just not one example! There are many organizations who switch their cloud deployment models amid the requirements demanded by the modern application users.
You could be also one of the people who is reconsidering their public cloud choices. Maybe because your needs have changed? Or you could be one wondering to re-architect an application? The possibilities are endless.
If you are one of those people, this blog is about you. There are many considerations which need to be overlooked when you’re deciding the architecture, technology and cloud deployment models for your application.
Before we move further, let’s discuss the four basic cloud deployment models:
What are Cloud Deployment Models?
Cloud Deployment models are a particular type of cloud environments. They are basically classified based on access, proprietorship and size of the environment. There are four types of cloud deployment models:
- Public Cloud Deployment Model
- Private Cloud Deployment Model
- Hybrid Cloud Deployment Model
- Community Cloud Deployment Model
There are many cloud deployment models, each one with their own specifications, benefits and trade-offs for the people who are reconsidering their options. Since each one should be used for the different set of requirements, it is highly critical for you to have a clear understanding of your options. Let’s discuss all of them.
#1. Public Cloud Deployment Model
This is the type of cloud deployment model in which the infrastructure is open for anybody to use. This has a distinct benefit of being more secure than transferring information via the Internet. This also costs less than the private cloud deployment model since the services are more commoditized. This is used by businesses which require on-demand scaling, for example, social networking, CRM, storage, etc.
#2. Private Cloud Deployment Model
This is a type of cloud deployment model in which the infrastructure is provisioned exclusively for a single company which may or may not include consumption through multiple business units.
It can be either on or off premises. This type of cloud deployment model is incorporated when sensitive or mission-critical information is involved. This allows for higher security, reliability and performance. This is mostly incorporated by governments and scientific laboratories.
#3. Hybrid Cloud Deployment Model
This consists of two or more than two cloud deployment models. All these models are unique but are bound by certain standard protocols. There are very few companies which have the ability to switch over all of their technology stacks to the cloud in one go.
For such companies, a hybrid cloud deployment model provides a smooth transition with the mix of on-premise and cloud options. NASA uses a hybrid cloud deployment model. For example, Nebula, an open-source cloud computing project employs a private cloud for research and development purposes while uses the public cloud to share datasets with the external public and partners.
#4. Community Cloud Deployment Model
This is a type of a cloud deployment model in which the cloud hosting is shared between many companies provided that they are operating within the same domain, for example, banking, government, educational institution, etc.
In other words, a group of several companies share a multi-tenant setup where they have the same privacy, security and performance concerns. This is used by businesses in joint ventures and research firms that require centralized cloud computing systems.
This was to give you a basic idea of the cloud deployment options that are available to you. But selecting the right one for your business is a tricky subject. Foremost of them, it highly depends on your application architecture. Especially when there are multiple viable and noble options depending upon whether you’re a well-established organization with million users or you’re a startup who are trying to build their MVP.
In this blog, we are going to discuss that! Which architecture you should opt for? What are the possible cloud deployment models available? What are the pros and cons? Let’s figure out.
Considering the above flowchart, I assume you have got a better idea about which cloud deployment model you want to use. However, we will discuss each of the points in detail in the following section.
#1. Right Way: Monolithic & Stateful Architecture
Before starting, we assume that you already have a functional app. If not, #2 would be appropriate for you to get started.
The first aspect that we will be evaluating is whether you want to keep the application monolithic and stateful.
Monoliths will require you to compose all of your application within a single piece. It is designed to be served as a self-contained. Different components of the program are interdependent and interconnected to each other.
If any program is to be updated, the whole application has to be rewritten.
However, monoliths have their own benefits. They have better throughput than other architectural patterns. These are also easier to test and debug.
After all these considerations, do you want to keep your architecture monolithic? Are these benefits tops on the list of your application priority? This leads us to our next point.
In case, if you already have an application and want to re-architect the app, may be opting for updated architectural patterns would be the best solution.
#2. When Re-architecting is Not an Option
In this section, we will discuss two types of cases. Firstly, what option do you have available if you are not ready to re-architect the app? Secondly, what if you are getting started from scratch?
To discuss the second case, we assume that the application’s first build would be a minimum viable product (MVP). Thus your goal would be to produce an efficient application with faster time to market. In this case, a monolith architecture will be suitable.
Another consideration is feature changes. Sometimes, you’ll be at a place where you’ve bootstrapped an app with well-written specifications and abruptly, there’s a change in one of the specs. This is easy with monolithic.
Here is an interesting image by Martin Fowler which states why going directly to microservices is a dangerous thing:
Coming to the case where you don’t want to re-architect the app, opting for a service-oriented architecture would be a better solution.
Mark Richards in his book Microservices vs Service Oriented Architecture suggests that SOA is better suited for comparatively large, enterprise-wide systems that require integration with many heterogeneous applications and services.
#3. Public/Private Cloud Options: VMs vs IaaS
Public clouds are the most common way of deploying your applications since everything is owned and operated by third-party service providers.
Or maybe if you want to increase your current capacity without much investment, IaaS provides you with the on-demand services. Saying this, it is ideal for a small startup of a mid-sized business.
Another reason being there are no upfront costs to get yourself started and you pay for what your use. Resources are available on-demand to meet your business needs. Some of the IaaS providers are AWS, DigitalOcean, Microsoft Azure, Rackspace Open Cloud, Alibaba Cloud, Google Compute Engine, etc.
If you do not want to opt for public cloud offerings, VMs could be a probable solution for you. They are suitable if latency is a key factor in your application.
Some of the prominent VMs providers are RedHat, Oracle, VMWare and many more.
#4. Analyzing Requirements of Event-driven Architecture
An event-driven architecture is a development technique where your architecture orchestration will revolve around the production, detection and consumption of events along with the responses they evoke. It works on the principle of event publisher/subscriber and with an event manager which is a middleman between both.
A few questions to ask before you consider opting for event-driven architecture:
-
Is your current app a small and usable building block? Can it be broken down into small blocks any further?
- The outputs and inputs of all the blocks are well-defined?
-
Would you be able to use current development and build tools?
- Does the serverless platform you’re opting for supports the following:
#1. Receiving and sending connections from the current APIs/clients?
#2. Interacting with the current database?
#3. Languages you’ve hands-on experience with?
If you think event-driven architecture is your potential option, you may go for microservices or serverless deployment options about which we will discuss in the next section. If no, modular/microservices are better-suited options for you? Let’s find out.
#5. FaaS Deployment Options: Public vs Private
Now that you’ve decided to opt for event-driven architecture. There are many serverless providers which offer functions-as-a-service on pay-per-use billing model. Or you can also opt for open source options to use at your on-premise data-centres.
FaaS has its own benefits like easy integration, no resource management, extremely inexpensive (based on your usage though) and on-demand scalability. Some of the major FaaS providers are AWS Lambda, Azure Functions, Cloud Functions, IBM Cloud, Nano Lambda, Syncano, Twilio and many more.
However, if you deploy functions on your on-premise data centres, you have the freedom of unlimited runtime and no cold start issues. Here are some of your options: Apache OpenWhisk, Oracle Fn Project, Kubeless, OpenFaas, Nuclio and many more. For more information on open source serverless options, visit here: Today in Serverless & Open Source
Before you opt for either of the cloud deployment model: public or private cloud, I’d suggest having a basic understanding of how vendor lock-in can affect your choices.
For example, if you’ve got a common standard then you have multiple vendors to choose from. If you’re self-hosting and can run on any x64 box then it doesn’t matter if HP (say) decides they want to increase the price of their servers, you just buy them from somewhere else.
If you’ve got a rack of servers in a managed datacenter you can put them in a truck and move them to another rack at a competitor (the rack fittings are standard). If you’re running basic VM images and AWS prices rise then you can take those same images to Azure.
But there’s not yet anything like that for lambda: code that uses lambda only runs on AWS because there’s no common standard for the serverless/lambda interface, each cloud provider has their own way of doing it.
#6. Modular Services & Microservices
Microservice architecture is a distinct method of developing the software/apps wherein organizations who want to work into an agile environment and move towards DevOps and continuous testing.
This allows you to create scalable solutions. It enables you to deliver new features into their live production systems multiple times a day, as well as to support web-scale traffic volumes. This is one of the major reasons why tech giants like Netflix, eBay, Amazon, Twitter, PayPal and many others work upon this.
Hence, here are the 5 point roadmap you need to answer to analyze whether you’re microservice ready or not?
 Whether or not you have a strong and disciplined architectural practice.
- Do you have a mature agile and DevOps practice? Is your data management team supporting it?
- If yes, take the process slowly. Initially, use the microservices only for the part where you need it, for examples, in applications with scalability and web-scale agility requirements.
- What will be your deployment environments? About which we will talk further. However, you will need to supplement it with capabilities such as API gateways, service discovery, monitoring and telemetry, build and test automation, messaging and data persistence.
- If your team isn’t ready to adopt the microservices architecture in its entirely, consider using modular services to gain relevant experience and significantly agility benefits.
Modular services, on the other hand, are loosely coupled arrangement of objects. This has no specific architecture like SOA, however, do not have the granularity as much as microservices. All the objects are defined according to business capabilities. All these objects interact with each other through the well-defined common interface.
If this sounds good for you, let’s analyze this aspect with little more considerations in the next section.
#7. Microservices Deployment: Containers & Orchestration
The most adapted cloud deployment model for microservices architecture is containers. Containers help you in having a focused business scope to meet agility in the development and delivery of services.
If you want to use containers on your on-premise data centre or your private cloud, your options include OpenShift Container Platform, Pivotal Container Service or other Kubernetes based platforms.
On the other hand, if you’re ready to use the public cloud, your container management options include AWS Fargate, Azure Container Instances, Google App Engine Flex, OpenShift Online, etc. These are simplified container management options at a lower cost but with a loss of control. If you need high control over your container management, your options include Amazon EKS, Google GKS and Azure AKS.
Nevertheless, in the below section, we will have a walk through what technology can be used under what prerequisites and where.
When to use VMs?
VMs are the heart of cloud computing. Significant adoption of virtualization has led to major changes in the processor architecture. Let’s discuss some of the scenarios where you should be using VMs:
-
High Security: With no added alterations, virtual machines provide greater security than other cloud deployment models. The major advantage here is that it has isolated hardware. On the other side, containers have shared kernel resources and application libraries. That says if security is an adamant prerequisite for you, VMs would be the best solution.
- Disaster Recovery: Major benefit of VM is the low turnaround time for site recovery and high availability. Although physical infrastructure also offers these capabilities, however, virtualization offers a wide range of options.
- Portable Environments: VMs are basically self-contained packages which are transferable. Virtualization offers you the ability to easily move from one server to another. This is highly critical when you want to balance the workload.
- Testing Environment: VMs offers multiple test environments. With VMs you can try different operating systems or may just different versions of a single operating system rather than having different computers for each one of them.
When to use IaaS?
IaaS in other sense is a virtual data centre. It can be used for a variety of purposes. You can install and make use of the operating system you like.
-
Temporary Workload: Unlike VMs, servers, storage, network resources and data centre is managed by the service providers. This makes IaaS a perfect option to opt for in cases where you want to deal with the temporary workload (say, during Christmas or Black Friday Sale) without much of a hassle. This lets you extend your data centre capabilities.
- Data analysis: To perform a huge analysis of the data, large computing power is required. IaaS provides you with the option to buy that computing power on demand in the most economical way.
- Networking Services: IaaS provides you to expand your networking services whenever the complexity grows. This could be for a short-term big data project or to support the ongoing services. This can free up internal IT staff for other priorities.
- High Scalability: Instead of investing upfront in purchasing the hardware, IaaS provides you with the virtualized computing resources. This lets you scale your workloads as you move forward on the pay-for-use model.
When to use Containers?
If you are a mid-sized company and wants to run an application without the need for virtual machines, containers are for you.
- Single OS: If you want to make use of an operating system of your own choice and want full control over your runtime language and version, containers work for you. Containers are great to start with when you want to run your software with specific version requirements.
- High Agility: Containers are comparatively faster than virtual machines because they have less overhead. This makes the development teams move fast, deploy software efficiently and operate at an unprecedented scale.
- Isolated & consistent environment: Containers provide a predictable environment to the development teams which are isolated from the other applications. These can also include software libraries, programming language runtimes and software dependencies with itself.
- High Portability: Containers can run anywhere you want which includes operating systems like Linux, Windows and Mac. On VMs or bare metal, in data centres on-premise or cloud and, of course, on the developer’s machine. This provides a highly portable environment for you to run your software.
It’s kind of embarrassing when you’re the premiere cloud infrastructure vendor in the world and you can’t keep up with demand on your own major shopping day, when scaling on demand is, you know one of the primary benefits of IaaS. — Ron Miller (@ron_miller) July 18, 2018
Let’s understand more from the example of Github moving to Container Orchestration Framework: As the number of services they ran increased, the SRE team began supporting similar configurations for dozens of other applications, increasing the percentage of our time we spent on server maintenance, provisioning, and other work not directly related to improving the overall GitHub experience.
New services took days, weeks, or months to deploy depending on their complexity and the SRE team’s availability. Over time, it became clear that this approach did not provide our engineers with the flexibility they needed to continue building a world-class service.
Their engineers needed a cloud deployment model which acts as a self-service platform they could use to experiment, deploy, and scale new services. Thee also needed that same platform to fit the needs of their core Ruby on Rails application so that engineers and/or robots could respond to changes in demand by allocating additional compute resources in seconds instead of hours, days, or longer.
When to use Serverless?
If you’re a company already using microservices and want to take advantage of the event-driven architecture, serverless is your option. This is especially true when you’re dealing with unpredictable traffic, releasing quicker updates and anything in between.
-
Variable and irregular workloads: For example, a website which doesn’t get much or any traffic during the nights. Since you’re required to pay only for the time your code runs, this is a potential cost optimizer.
-
Critical developer productivity: If you do not have the example in either Serverless or Containers, serverless would be a far better option for you to ship your Hello World application. However, if you want to run a complex application, a container-based application is a better option. When you use containers, you will have to create a cluster, configure your orchestration framework and then deploy the first container. On the other hand, serverless platforms use web CLI provided by the cloud provider to get started in minutes.
-
In-built auto-scalability: The greatest benefit of serverless is its auto-scalability. If you’re a developer you probably don’t want to manage anything. FaaS providers manage everything for you and provide seamless auto-scalability.
-
Frequent updates: Serverless doesn’t require you to upload your code to the servers or do any backend configuration in order to release a working version of an application. This lets you release quicker releases on your product. You can either upload code all at once or a function at a time since your application isn’t a monolithic stack but rather a collection of functions provisioned by the vendor.
Take Away!
While you evaluate your cloud deployment model, it is critical to consider your application architecture as well. If you haven’t already upgraded your application architecture and respective cloud deployment model, it’s likely just a matter of time before you do.
Choosing the best possible deployment options for your business is vital to your company’s success, meaning you need to fully understand all the advantages and disadvantages of various options to make the right choice.


detectExcursion.Rmd
This (in development) vignette will showcase a simple example of
detecting an excursion – defined in actR as a short term deviation from
a baseline – using the classic NGRIP d18O record. This dataset comes
preloaded in actR, and you can learn more about it with
?ngrip
.
actR uses an excursion detection algorithm developed and used by Morrill et al. (2013).
library(actR)
First, let’s take a look at the excursion in the NGRIP d18O dataset
near 8200 yr BP (the 8.2 ka event). First, we’ll tell it not to simulate
any uncertainties. We’re using the detectExcursion()
function, a high-level function for this approach.
We’ll need to give it some key parameters, in this case, we need to define the center and width of the event window (8200 and 200 yr, respectively). We also need to define the reference intervals (400 yrs on each side), and how many standard deviations to use for the baseline (sig.num = 2). We’re only looking for cold (negative excursions).
excOutNoUnc <- detectExcursion(ngrip,
vals.variable.name = "d18O",
event.yr = 8200,
event.window = 200,
ref.window = 400,
sig.num = 2,
exc.type = "negative",
n.ens = 1,
null.hypothesis.n = 1,
simulate.time.uncertainty = FALSE,
simulate.paleo.uncertainty = FALSE)
#> Selected d18O
#> Testing null hypothesis with 1 simulations, each with 1 ensemble members.
#> This will probably take about 1 minutes
After it runs, the summary()
or print()
functions will summarize the results.
summary(excOutNoUnc)
#> NGRIP.NGRIP.2004 - d18O: Excursion test results
#> Searched for negative excursions in a 200 year window around 8200 BP, with reference windows of 400 years on either side.
#>
#> Overall result: Empirical p-value = 0
#> Time uncertainty considered? FALSE
#> Paleo uncertainty considered? FALSE
#> Error propagation ensemble members = 1
#> Null hypothesis testing ensemble members = 1
#>
#> Parameter choices:
#> sig.num = 2
#> n.consecutive = 2
#> exc.type = negative
#> min.vals = 8
#> na.rm = TRUE
Here we see the results, but it’s easier to see the results, especially without uncertainties, as a plot:
plot(excOutNoUnc)
Here we see the baselines defined as red lines, with the excursions
and the points that exceed the threshhold as red dots. The method
requires a minimum number of consecutive points to pass the threshold,
the default is 2, this parameters is n.consecutive
.
So there’s clearly and excursion centered on 8.2 ka, but how much might this be affected by uncertainty in the y-axis? In this case we’re not really thinking of measurement error, but of the myriad uncertainties that complicate the relationship between d18O and climate (in this case, we’re thinking mostly of cooling). actR propagates these uncertainties as ensembles, and works best if you pass a full ensemble as input. However most datasets don’t have paleo ensembles, so actR will simulate an ensemble based on an uncertainty estimate (0.2) and uncertainty AR1 coefficient (0.8).
Now, because we’re working with ensembles, this will run multiple
times to propagate the error. We specify the number of error propagation
ensemble members in n.ens
. To keep this vignette snappy,
we’re only using 50, but you probably want 100 or more.
We’re also going to take advantage of actR’s robust null hypothesis testing, which will repeat the full analysis for synthetic data that mirrors the characteristics of the input data. Check out this (TBD) to learn more about null hypothesis testing in actR. We’ll also run the null hypothesis simulation 50 times.
excOutUncY <- detectExcursion(ngrip,
vals.variable.name = "d18O",
event.yr = 8200,
event.window = 200,
ref.window = 400,
exc.type = "negative",
n.ens = 50,
null.hypothesis.n = 50,
simulate.time.uncertainty = FALSE,
simulate.paleo.uncertainty = TRUE,
paleo.uncertainty = 0.2,
paleo.ar1 = 0.8)
#> Selected d18O
#> Testing null hypothesis with 50 simulations, each with 50 ensemble members.
#> This will probably take about 1 minutes
This time, when we summarize the results, we get more interesting output, including an empirical p-value, which is the fraction of null hypothesis simulations that exceed the positivity rate in the actual data. Typically, empirical p-values less than 0.05 are considered significant.
summary(excOutUncY)
#> NGRIP.NGRIP.2004 - d18O: Excursion test results
#> Searched for negative excursions in a 200 year window around 8200 BP, with reference windows of 400 years on either side.
#>
#> Overall result: Empirical p-value = 0.04
#> Time uncertainty considered? FALSE
#> Paleo uncertainty considered? TRUE
#> Error propagation ensemble members = 50
#> Null hypothesis testing ensemble members = 50
#>
#> Parameter choices:
#> sig.num = 2
#> n.consecutive = 2
#> exc.type = negative
#> min.vals = 8
#> na.rm = TRUE
We also get a bit more information when plotting with uncertainties. This plotting routine will show the data with uncertainty bands in the back, and a representative ensemble member illustrating the excursion.
plot(excOutUncY)
Including d18O uncertainty muddies the water a bit, but this feature is still clearly robust to uncertainties in the paleo data. But what about age uncertainty?
In addition to d18O uncertainty, age uncertainty has the potential to obscure abrupt changes in datasets. When we’re testing an excursion to see if it’s robust to age uncertainty, we’re examining the likelihood that, given age uncertainty, the excursion occurred during this specific 200 year window, NOT whether the excursion occurred at all. Age uncertainty is unlikely to destroy an excursion this pronounced, but may be able to move it from 8.2 to 8.5 ka.
Again, actR is designed to work with ensembles, so entering an age
ensemble in to detectExcursion()
is ideal. But as is often
the case, this NGRIP dataset doesn’t come with ensembles. In this case,
actR can reasonably simulate age uncertainties (especially for layer
counted records) using BAM (Comboul et al. 2015). Here we’ll use BAM
with a Bernoulli model and symmetric 0.02 over/undercounting rates.
Once again, we’ll run all of this, including the age uncertainties, through our robust null hypothesis testing.
excOutUncXY <- detectExcursion(ngrip,
vals.variable.name = "d18O",
event.yr = 8200,
event.window = 200,
ref.window = 400,
exc.type = "negative",
n.ens = 50,
null.hypothesis.n = 50,
simulate.time.uncertainty = TRUE,
bam.model = list(ns = 50, name = "bernoulli", param = 0.02),
simulate.paleo.uncertainty = TRUE,
paleo.uncertainty = 0.2)
#> Selected d18O
#> Testing null hypothesis with 50 simulations, each with 50 ensemble members.
#> This will probably take about 1 minutes
Let’s examine the result.
summary(excOutUncXY)
#> NGRIP.NGRIP.2004 - d18O: Excursion test results
#> Searched for negative excursions in a 200 year window around 8200 BP, with reference windows of 400 years on either side.
#>
#> Overall result: Empirical p-value = 0
#> Time uncertainty considered? TRUE
#> Paleo uncertainty considered? TRUE
#> Error propagation ensemble members = 50
#> Null hypothesis testing ensemble members = 50
#>
#> Parameter choices:
#> sig.num = 2
#> n.consecutive = 2
#> exc.type = negative
#> min.vals = 8
#> na.rm = TRUE
And plot the output.
plot(excOutUncXY)
Given these uncertainties, and all of our parametric choices, we rest assured that this excursion is robust to our uncertainties.


Industrial Injuries Disablement Benefit
- 1. Overview
- 2. What you'll get
- 3. Eligibility
3. Eligibility
Accidents
You may be able to claim Industrial Injuries Disablement Benefit (IIDB) if:
- you were employed when the accident or event happened
- you were on an approved employment training scheme or course when the accident or event happened
- the work accident or event that caused your illness or disability happened in England, Scotland or Wales
There are some exceptions you can ask your regional Industrial Injuries Disablement Benefit Centre about.


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Kormangla
Electronic city
Manipal
Hubli
Manglore
Gulbarga
Davangere
Telangana
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Adilabad
Warangal
Nalgonda
Suryapat
Maharasta
Mumbai
Pune
Nagpur
Nashic
Panvel
kolhapur
Chhattisgarh
Raipur
Atalnagr
Bhilai
Bilaspur
Rajnandgaon
Ambikapur
Jharkhand
Ranchi
Daltanganj
Latehar
Garhwa
Dhanbad
Bokaro
Online Job Application Form
Personal Information
Full Name
Father's Name
Mother's Name
Current Address
Permanent Address
Mobile Number
Date of Birth
Select Gender
Male
Female
Other
Highest Qualification
Year of Passing
Languages Known
Telugu
English
Hindi
Nationality
Indian
American
others
Aadhar Card Number
Pan Card number


Put simply, arrays are ordered collections of objects. Each collection can hold multiple elements of any data type, including other arrays. Each element can be referred to by an index. An important thing to keep in mind is that arrays start at the zeroth index. The last item in an array can be referred to by the -1 index (accordingly, the penultimate array item would be indexed at -2). There is more than one way to create an array. For example...
Creation with square brackets:
cheese = ["brie", "gruyere", "swiss"]
#cheese[0] cheese[1] cheese[2]
#The respective index positions.
cheese = Array.new
cheese.push "brie"
cheese.push "gruyere"
cheese.push "swiss"
puts cheese
=> brie
gruyere
swiss
A hash can be thought of as a database or a collection that contains key value pairings. Keys are unique, and each key is to be assigned a value. They're also known as associative arrays due to their similarities to arrays, but they differ in that arrays index content by integers, while hashes allow content to be indexed using any object type. Lets take a look at a couple ways we can create a hash...
Creation with curly braces:
#Option 1 formatting:
cheese = {"brie": "mildly tangy and fruity", "gruyere": "creamy and sweet", "swiss": "slightly nutty and mild"}
#Option 2 formatting:
cheese = {"brie" => "mildly tangy and fruity", "gruyere" => "creamy and sweet", "swiss" => "slightly nutty and mild"}
cheese = Hash.new
cheese["brie"] = "mildly tangy and fruity"
cheese["gruyere"] = "creamy and sweet"
cheese["swiss"] = "slightly nutty and mild"
puts cheese
=> {"brie"=>"mildly tangy and fruity", "gruyere"=>"creamy and sweet", "swiss"=>"slightly nutty and mild"}
cheese["brie"]
=> "mildly tangy and fruity"
cheese.values
=> ["mildly tangy and fruity", "creamy and sweet", "slightly nutty and mild"]


AN AMERICAN WEREWOLF IN PARIS
A Film Review by James Berardinelli
RATING: *1/2 OUT OF ****
United States/France/UK, 1997 U.S. Release Date: 12/25/97 (wide) Running Length: 1:40 MPAA Classification: R (Violence, gore, profanity, nudity) Theatrical Aspect Ratio: 1.85:1
Cast: Tom Everett Scott, Julie Delpy, Vince Vieluf, Phil Buckman, Julie Bowen, Pierre Cosso, Tom Novembre, Thierry Lhermitte Director: Anthony Waller Producer: Richard Claus Screenplay: Tim Burns & Tom Stern and Anthony Waller Cinematography: Egon Werdin Music: Wilbert Hirsch U.S. Distributor: Hollywood Pictures
AN AMERICAN WEREWOLF IN PARIS is a failed attempt to recapture the humor and horror of John Landis' 1981 feature, AN AMERICAN WEREWOLF IN LONDON. Where the original had comedy, the sequel has the kind of revolting silliness that can be found in TV sit-coms. Where the first installment had chills, this one has sequences that are inappropriately, unintentionally funny. In short, while AN AMERICAN WEREWOLF IN LONDON has become something of a minor classic in its genre, the woeful AN AMERICAN WEREWOLF IN PARIS seems destined for late nights on Cinemax (it even has the necessary gratuitous nudity).
The film opens with a trio of daredevil Americans -- Andy (Tom Everett Scott), Brad (Vince Vieluf), and Chris (Phil Buckman) -- sneaking up to the top of the Eiffel Tower to drink wine and do a little bungee jumping. Soon they have company in the person of Serafine (Julie Delpy), who has decided to end it all. She jumps, but Andy, with a bungee cord attached, goes after her, and manages to save her (at the price of a major headache). Serafine disappears, but a smitten Andy seeks her out. However, once he learns his would-be girlfriend's dark secret, he wishes he hadn't. She's a werewolf, cursed to change into a hideous beast when the moon is full, and, to make matters worse, he has suffered a nasty bite himself.
Actress Julie Delpy is far too good for this movie. She imbues Serafine with spirit, spunk, and humanity, which gives us an emotional stake in the character's fate. This isn't necessarily a good thing, since it prevents us from relaxing and enjoying AN AMERICAN WEREWOLF IN PARIS as a completely mindless, campy entertainment experience. Delpy's injection of class into an otherwise classless production raises the specter of what this film could have been with a better script and a better cast surrounding her.
Delpy's previous credits include such memorable ventures as Krzysztof Kieslowski's WHITE and Richard Linklater's BEFORE SUNRISE. She was radiant, charismatic, and effective in both. Given the nature and level of the material she has to work with here, she gets as close as possible to those adjectives. It could be argued that Delpy is the only reason to see AN AMERICAN WEREWOLF IN PARIS, but even her most devoted fans should consider giving this one a miss. And if your primary objective is catching a glimpse of her in the buff, check out either KILLING ZOE or THE PASSION OF BEATRICE -- those movies have intelligible plots in addition to breasts.
The rest of the cast acts at a level considerably below that of Delpy -- which is to say, they give performances appropriate for the screenplay. Tom Everett Scott (THAT THING YOU DO) plays the lead like he's in a made-for-TV movie. It would be kind to call him bland. Actors Vince Vieluf and Phil Buckman, as Andy's friends, are no more impressive. Julie Bowen (HAPPY GILMORE) is suitably fetching as werewolf meat. And respected French actor Thierry Lhermitte has a brief turn as another monster meal.
On the technical side, it's all bad news. The computer-generated werewolves look painfully unreal. The creatures would probably have been more believable had they been men in wolf suits. Repeated use is made of the "werewolf cam", an infrared wolf's point-of-view approach that's interesting the first couple of times it's employed, then becomes tedious. And the soundtrack includes some alternative grunge rock tunes that clash violently with the on-screen action they're matched to.
Director Anthony Waller, who displayed a confident, edgy style in MUTE WITNESS, stumbles with this material, never being able to make the comedy and horror elements gel.  (I will give Waller credit for killing off a dog, though -- something that's rarely seen in movies these days.) AN AMERICAN WEREWOLF IN PARIS is marginally entertaining in a "bad movie" sort of way, but that's a dubious distinction. Ultimately, it's an unfortunate effort, for, while it isn't unbearable to sit through, it isn't a howl, either.
Copyright 1997 James Berardinelli
- James Berardinelli e-mail<EMAIL_ADDRESS>Now with more than 1300 reviews... The ReelViews web site: http://movie-reviews.colossus.net/
"No art passes our conscience in the way film does, and goes directly to our feelings, deep down into the dark rooms of our souls"
- Ingmar Bergman
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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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


PREFACE
INSTALLATION
SOMMAND SHELL
CLOUD SERVICES
openapi
MANUAL PAGES
DATABASE
BENCHMARKS
CODE
CONTRIBUTION
CLOUD ACCOUNTS
Outdated:


Welcome to mini_net’s documentation!¶
- Author: Cole Howard
- Email<EMAIL_ADDRESS>network.py is a basic implementation of a one layer linear neural network, to examine an implementation of backpropagation. It is based on the basic model of the Perceptron. Information on that can be found at: https://en.wikipedia.org/wiki/Perceptron
The intent of this specific project is to alter the Perceptron’s
decision function to a logistic function and add a “backpropagation” step at the end of each vector’s pass through the neuron.
There are several methods included that are currently passed, as they are plans to make it extensible as possible as a tool for exploring neural nets under more general circumstances.
- Dependencies:
numpy.dot() : for a fast implementation of the dot product of two vectors sklearn.datasets : (optional) for running this as a script on the
scikit-learn digits dataset- neuron : the class definition of an individual neuron, also included in
- mini_net
- Usage:
It is currently set up to run a training set of input (along with the associated answers) and a set of similar, but distinct, input (without) the answers, and have the machine guess an answer to each of those cases based on information it gathered during the training run.
To import the network for testing on other data:
download the package mini_net, then include in your script:
from network import NetworkTo execute as is, from the command line, while in the linear_neuron/mini_net/ directory, input:
$ python3 network.pyThis will pull the learning and test data from scikit-learn run both and return a success count, total count of the unseen test data, and the success rate that equals.
First output and success ratio will be based on the first set of testing vectors. The second set will represent the same for the validation set. The visualization (see below) that pops up, just close that window for the script to finish running.
- Alternate data sets:
- Alternate training and testing data sets can be swapped out in the first section of main() below. See those notes for specifics.
- Visualization:
- Pyplot is included to provide a visual representation of a member of the dataset.
-
class
network.
Network
(neuron_targets, train_set, train_answers, epochs, test_set, test_answers, validation_set, validation_answers, images=None)¶ A Network instance will create layers of neurons for the implementa- tion of neural network.
Parameters: - images (list) – Corresponding images of the dataset
- neuron_targets (list) – The possible final output values
- vector_size (int) – Size of the individual input vectors
- train_set (list) – Set of vectors for the learning portion
- train_answers (list) – Correct answers that correspond to the train_set
- epochs (int) – Number of times the learn_run will run for a given train_set
- test_set (list) – Set of vectors, discrete from the train_set to have the machine guess against
- test_answers (list) – Correct answers for the test_set, to compare the machine’s guesses against
- validation_set (list) – A validation set to compare answers in a second run
- validation_answers (list) – Answer for the above
-
neurons
¶ Class Neuron
Instances of the Neuron class, one for each of possible correct answers
-
append_bias
(vector)¶ Takes a list of n entries and appends a 1 for the bias
Parameters: vector (list) – -
num_of_training_vectors
¶ int
This is to adjust the size of the training set when all of the data is provided as large list. Breaking the training data into a training set, testing set, and a validation set. Picking this number is a balance between speed (lower number) and overfitting the data (a higher number)
Returns: The input vector with a one appended to the end of the list, as a bias Return type: list -
-
gradient_descent
(vector, vector_index)¶ Calculates the gradient_descent
Parameters: - vector (list) – A single input, comprised of floats
- vector_index (int) –
-
learning_rate
¶ float
Determines how much of the error is applied to the weights in each iteration
Returns: Represents the error to be used to update the weights of the neurons. It should approximate a gradient descent in topology of the outputs Return type: float
-
learn_run
()¶ Runs an iteration through the neuron sets and adjust the weights appropriately. It then follows up with a second weight adjusment accross all neurons with an estimate of the gradient descent function
-
report_results
(guess_list, validation=False)¶ Reports results of guesses on unseen set
Parameters: guess_list (list) –
-
run_unseen
(validation=False)¶ Makes guesses on the unseen data, and switches over the test answers to validation set if the bool is True
For each vector in the collection, each neuron in turn will either fire or not. If a vector fires, it is collected as a possible correct guess. Not firing is collected as well, in case there an no good guesses at all. The method will choose the vector with the highest dot product, from either the fired list or the dud list.
Parameters: validation (bool) – Runs a different set of vectors through the guessing process if validation is set to True Returns: a list of ints (the guesses for each vector) Return type: list
Author: Cole Howard Email<EMAIL_ADDRESS>neuron.py is a basic linear neuron, that can be used in a perceptron Information on that can be found at: https://en.wikipedia.org/wiki/Perceptron
It was written as a class specifically for network ()
- Usage:
From any python script:
from neuron import Neuron- API:
- update_weights, fires are the accessible methods usage noted in their definitions
-
class
neuron.
Neuron
(vector_size, target, sample_size, answer_set)¶ A class model for a single neuron
Parameters: - vector_size (int) – Length of an input vector
- target (int) – What the vector will associate with its weights. It will claim this is the correct answer if it fires
- sample_size (int) – Total size of sample to be trained on
- answer_set (list) – The list of correct answers associated with the training set
-
threshold
¶ float
The tipping point at which the neuron fires (speifically in relation to the dot product of the sample vector and the weight set)
-
weights
¶ list
The “storage” of the neuron. These are changed with each training case and then used to determine if new cases will cause the neuron to fire. The last entry is initialized to 1 as the weight of the bias
-
expected
¶ list
Either 0’s or 1’s based on whether this neuron should for each of the vectors in the training set
-
guesses
¶ list
Initialized to 0, and then updated with each training vector that comes through.
-
fires
(vector)¶ Takes an input vector and decides if neuron fires or not
Parameters: vector (list) – A sample vector Returns: - bool – Did it fire? True(yes) or False(no)
- float – The dot product of the vector and weights
-
train_pass
(vector, idx)¶ Passes a vector through the neuron once
Parameters: - vector (a list) – Training vector
- idx (an int) – The position of the vector in the sample
Returns: Return type: None, always
-
update_weights
(error, vector)¶ Updates the weights stored in the receptors
Parameters: - error (int) – The distance from the expected value of a particular training case
- vector (list) – A sample vector
-
l_rate
¶ float
A number between 0 and 1, it will modify the error to control how much each weight is adjusted. Higher numbers will train faster (but risk unresolvable oscillations), lower numbers will train slower but be more stable.
Returns: Return type: None


{% include common/about.html %}
{% include common/install.html %}
{% include common/bundling.html %}
{% include common/flags.html %}
{% include common/cdn.html %}
{% include common/themes.html %}
{% include common/animation.html %}
{% include common/badge.html %}
{% include common/breadcrumbs.html %}
{% include common/button.html %}
{% include common/carousel.html %}
{% include common/checkbox.html %}
{% include common/color.html %}
{% include common/combobox.html %}
{% include common/cta-button.html %}
{% include common/details.html %}
{% include common/drawer-dialog.html %}
{% include common/fullscreen-dialog.html %}
{% include common/eek.html %}
{% include common/expand-button.html %}
{% include common/field.html %}
{% include common/filter-button.html %}
{% include common/filter-menu.html %}
{% include common/filter-menu-button.html %}
{% include common/floating-label.html %}
{% include common/global.html %}
{% include common/icon.html %}
{% include common/icon-button.html %}
{% include common/infotip.html %}
{% include common/inline-notice.html %}
{% include common/less.html %}
{% include common/lightbox-dialog.html %}
{% include common/link.html %}
{% include common/listbox.html %}
{% include common/listbox-button.html %}
{% include common/marketsans.html %}
{% include common/menu.html %}
{% include common/menu-button.html %}
{% include common/page-grid.html %}
{% include common/page-notice.html %}
{% include common/pagination.html %}
{% include common/panel-dialog.html %}
{% include common/program-badge.html %}
{% include common/progress-bar.html %}
{% include common/progress-spinner.html %}
{% include common/progress-stepper.html %}
{% include common/radio.html %}
{% include common/section-notice.html %}
{% include common/section-title.html %}
{% include common/select.html %}
{% include common/signal.html %}
{% include common/snackbar-dialog.html %}
{% include common/svg.html %}
{% include common/switch.html %}
{% include common/tabs.html %}
{% include common/textbox.html %}
{% include common/toast-dialog.html %}
{% include common/tooltip.html %}
{% include common/tourtip.html %}
{% include common/typography.html %}
{% include common/utility.html %}
{% include common/window-notice.html %}


A born and raised Seattlite somehow landed in Salt Lake City, I'm taking advantage of every opportunity this beautful state has to offer me; affordable education, versatile outdoor activity at your fingertips and 222 days of sunshine, to name a few. I love the outdoors, travel, live music, good humor, meeting new people and experiencing new culture and cuisine. I'm on the hunt and ready to commit to a career that will pull me out of the office cubicle and push me out into the world. Hard work and a positive attitude are what I hope will git me where I strive to be!


This tutorial contains all the basics of every language
Practical examples illustrating each concept is done
References from popular autors' books
Examples for each concept is given
This is a coding practice platform.


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---
layout: post
title: 1/1
date: '2013-01-01T21:57:00.001-07:00'
author: Jacob .
tags:
modified_time: '2013-01-01T21:57:56.331-07:00'
blogger_id: tag:blogger.com,1999:blog-6400085873105072120.post-3467976365877223813
blogger_orig_url: http://www.svlark.com/2013/01/11.html
---
We got some nice new pictures of the girls' rooms for the house site, though we haven't gotten them posted yet. Audrey did a great job with whatever lesson it was that we did in TYCTR. Still really enjoying Live on the Margin.
Blogging is kind of a crazy thing. You can never make any assumptions about who might of might not be reading...


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Sem opções
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{% trans role_descr=role.role_description.title, organization=role.organization.printable_name %}Based on your e-mail address we have granted you a {{role_descr}} role on {{organization}}.{% endtrans %}
{% if contacts %} {% trans organization=role.organization.printable_name, contacts=contacts %}If you need extra permissions, please contact one of the profile managers for {{organization}}: {{contacts}}.{% endtrans %} {% else %} {% trans %}If you have any questions, please contact us.{% endtrans %} {% endif %}
{% else %} {# XXX We cannot use "We found a matching profile for your e-mail domain!" since we check emails even when there are no profile. #} {% trans %}Please verify your e-mail address before going further. To do so click on the link in the e-mail we just sent you.{% endtrans %} {% endif %}


Isar735.github.io
Math Tutoring - Mathnasium of Lorton - The Math Learning. Do my homework or write my assignment - no matters - We can help you with any assignment online - Order today and get your homework done. Expert writers Pay Someone To Do My Algebra Homework - Online Class Helpers.
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Popline is an HTML5 Rich-Text-Editor Toolbar.
Inspired by popclip. Compared to traditional RTE, popline will float around the selected text.
It provides commonly used editing features out-of-the-box.
Easy to extend, Easy to customize.
Two modes supported, Edit mode and View mode.
The default theme popline used is designed by @Pepsin, Thanks.
happy to use
@kenshin54


Who Uses Erowid?
An Analysis of Erowid's User Base
I thought it would be interesting to use some of the forum's metadata I scraped to profile the sites demographics and popularity. In the previous section, I noted that the 'average' sentiment value tracked the 'male' average very closely. This led me to believe that the site was male dominated. Gender data is readily available in the forums metadata, so I thought I would test this assumption.
Indeed, the websites population (or at least active user base) is mostly male. The most popular substances for female authors are MDMA and cocaine. I'm a little surprised that cannabis stories are so heavily male dominated, given the relative accessibility and popularity of the drug.
The forum posts list the published date. I thought it would be interesting to look for trends- what substances are becoming more popular to write about and which are fading from relevance. I went ahead and plotted this data below.
The results are difficult to anaylze. The spikes are seemingly random with no clear trendlines. I do think the spike of stories about amphetamines between 2005-2010 is interesting. I'm also curious about what prompted the huge spike in ketamine stories in 2018. At this time, I don't have the answers to these questions.
There's also a spike in cannabis stories in 2012-2014. This is the period when legalization of the substance occurred in Washington, Colorado, Oregon, and Alaska. Perhaps the incresed accessibility prompted the new writings.


| DiscreteWavelets Toolbox |
Wavelet shrinkage function for denoising
y = ShrinkageFunction(t,lambda)
y = ShrinkageFunction(t,lambda) creates the piecewise linear function used to perform wavelet shrinkage.
The value lambda must be a positive number.
ShrinkageFunction is piecewise linear with the following pieces:
| t+lambda, | if t < -lambda |
| 0, | -lambda < x < lambda. |
| t-lambda, | if t > lambda |
ShrinkageFunction(t,lambda) converts t to zero if |t|
Apply shrinkage to a vector. The commands
v=[ -3 2.1 1.8 -4 5 1 .5 -.1] %Create a vector y=ShrinkageFunction(v,2) %Apply the shrinkage function with lambda=2
produces
v = -3.0000 2.1000 1.8000 -4.0000 5.0000 1.0000 0.5000 -0.1000 y = -1.0000 0.1000 0 -2.0000 3.0000 0 0 0
Plot the shrinkage function.
t=-5:.001:5; %Create some evaluation points plot(t,ShrinkageFunction(t,2)); %Plot the shrinkage function title('\lambda = 2');
DonohoSure, SureShrink, UniversalThreshold, WaveletShrinkage
© 2007-2008 Patrick Van Fleet


Dear {{ firstName }} {{ lastName }}, follow the link to complete registration of your station
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This is a static timing analysis tool that gets the Verilog gate level net list of a digital circuit, the library of the cells used in the circuit, the net capacitances file, the clocks skew file and timing constraints (the clock period, input delays and output delays). The tool checks all the timing paths and reports the violating timing paths, if any.


Engaged as Data Infrastructure Engineering Lead to help Vanquis transform the business to a cloud delivered, automated, scalable and adaptable technology model while adopting agile/devops methodologies for prioritising and delivering banking systems to rival the challenger banks. Building new teams to enable full stack support for the most key area of the bank – the data solutions for Customers and Business Intelligence
Azure, VSTS, Pipelines, Terraform, Ansible, Docker, AKS, Azure Automation, Security, Monitoring, AD, Legacy Integration
As Head of Operations, I was able to modernise the companies production operations considering the global layout of the support teams and unify the disparate support processes ranging from outdated manual build processes, inflexible hardware allocation and wholly separate development to production entities to a fully integrated and as much as possible automated self-maintaining offering to allow the business to evolve at the rate it desires
Azure, AWS, Docker, Bare-Metal, Apache, Tomcat, Oracle, MySQL, Mesos Marathon
Supporting the evolution of the product and processes behind the worlds most popular online media resource for news, I worked with a team of 6 people to deliver MailOnline.co.uk/DailyMail.co.uk
Using a wide range of platforms based on solid Solaris, Linux, Windows and cloud platforms, utilising a whole range of technologies to provide a robust environment from development through to production.
The role is also responsible for production maintenance and support of numerous other DMGT online offerings, including Metro and Wowcher.


ABOUT
Zhijun (Kenneth) Yan
Home
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Dear Visitors & CS185 Instructors:
This website is for UCSB Spring2020 CS185. Designed and wrote by Zhijun (Kenneth) Yan
Wish all the best during this COVID-19 time and all the time.
Hope everything works out fine!
Best
Zhijun (Kenneth) Yan
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Since 1982, the arcade world record has been broken {{ timeline.arcadeWRTimeline.length }} times by {{ timeline.arcadeWRPlayerCount }} players.
Since 1998, the MAME world record has been broken {{ timeline.mameWRTimeline.length }} times by {{ timeline.mameWRPlayerCount }} players.
Since 1982, the combined platform world record has been broken {{ timeline.combinedWRTimeline.length }} times by {{ timeline.combinedWRPlayerCount }} players.


| Input |
|---|
| BAP (combo?) STAB? |
| Crit Stage 0 1 2 3 |
| Attack Rank |
| Ability Effects |
| Field Effects |
| Item Effect One |
| Defense Rank |
| Burn Effect? |
| Type Effectiveness |
|
Attacker's Attack Stage Boosts/Decrease |
|
Attacker's Defense Stage Boosts/Decrease |
| Item Effect Two |
| Output |
|---|
| RNG Roll Crit Roll |


deget/ddown; a downloader cli/api for dotnet
Jan 14, 2019 09:10 · 449 words · 3 minute read
Hello everyone, I would like to introduce my first global tool for dotnet core ecosystem. This is one of my side projects that I have completed in my free time. It is a downloader just like ‘wget’ command in unix. It has partitioning, pause and resume features. Also, it is not only a CLI tool, but has a nuget package that you can include to your projects for downloading files.
Install command
dotnet tool install -g deget
dotnet add package DDown
Example CLI command
deget https://github.com/OpenShot/openshot-qt/releases/download/v2.4.1/OpenShot-v2.4.1-x86\_64.dmg
I have started this project almost a year ago; My curiosity started with “can I do something like ‘free download manager’ in command line just like ‘wget’ command in unix”. I started digging into HTTP protocol documentation. And, I found “Range” option which I didn’t know before. With this option I can divide the file and download it with chucks.
So, the main idea behind the tool is to request the file information with only header option. After that, calculate the partitions and start download as a stream. I kept the partitions and the history files in user’s LocalApplicationData. Maybe, it can change in future.
Let’s see results.
Here is an example run command with four partitions.
deget [https://github.com/OpenShot/openshot-qt/releases/download/v2.4.1/OpenShot-v2.4.1-x86\_64.dmg](https://github.com/OpenShot/openshot-qt/releases/download/v2.4.1/OpenShot-v2.4.1-x86_64.dmg) -p 4
deget example output
I think, one of the most useful thing is that CLI downloads the file into current folder in which the command runs. But, if you want to change the download location you can just use -o flag. Also, if you want to download the file into user’s downloads folder you can just use -d flag. For more options,
\-p, — partition (Default: 0) Set partition count. Default zero means system’s processor count
\-o, — output Default value is current folder that command runs.
\-r, — override (Default: false) Override the lastest download file with same name. Otherwise it will download the file with numbers (e.g. File (1).exe, File (2).exe).
\-b, — buffersize (Default: 8192) Set buffer size.
\-t, — timeout (Default: 10000) Set timeout parameter in miliseconds.
\-d, — downloadFolder (Default: false) The file download location set to User’s download folder.
— help Display this help screen.
— version Display version information.
value pos. 0 Required. Link to download
DDown, API is ready to go, you can easily start with following lines for adding file download feature to your project. And, you can customize your options with DDown.Options class. For more information you can take a look at github.
using DDown;
var downloader = new Downloader(link);
var status = await downloader.PrepareAsync(); // status contain information about file. (e.g. Length, IsRangeSupported, PartitionCount)
await downloader.StartAsync();
if (!downloader.Canceled)
{
// this method will marge partitions
await downloader.MergeAsync();
}
Thank you for reading.


| Curators | LSDB URL |
| https://research.cchmc.org/LOVD/home.php | |
| David Baux | https://grenada.lumc.nl/LOVD2/Usher_montpellier/home.php |
| Belinda Giardine and Joseph Borg | The Globin Gene Server |
| Graeme Grimes, Isabel Hanson, Kathy Williamson and Veronica van Heyningen | http://lsdb.hgu.mrc.ac.uk/home.php |
| Ivy Jennes | http://medgen.ua.ac.be/LOVDv.2.0/home.php |
| Judy Savige and Helen Storey | https://grenada.lumc.nl/LOVD2/COL4A/home.php |
| Nancy Braverman and Steven Steinberg | http://www.dbpex.org/home.php |
| Paul van der Zwaag | http://grenada.lumc.nl/LOVD2/ARVC/home.php |
| Raymond Dalgleish | https://oi.gene.le.ac.uk/home.php |
We would also say special thanks to http://www.bluebison.net/, for his kind permission
for the usage of his drawing in our website.


Anyone who wishes to use the United Nations Decade on Biodiversity logo must follow the instructions in the Brand Usage Guidelines and must send us a signed waiver form. You can choose between a PDF form or an online form.
The electronic waiver is faster and paperless! To ensure that your information is secure, you need a CBD user account. After completing the waiver you should see a confirmation page where you can download the logo. If you don't see this page, please contact us for further information or assistance. Go to www.cbd.int/2011-2020/waiver/submit to access the online form. If you do not have an account or are not logged in your will be redirected to the accounts page.


Senator Mark Warner said the Senate's Russia investigators have requested records from the Treasury Department's Financial Crimes Enforcement Network (FinCEN) relating to Trump, campaign aides, and Administration officials.


{{! Great tutorial on mustache.js + node.js: http://devcrapshoot.com/javascript/nodejs-expressjs-and-mustachejs-template-engine }}
FYI: Currently Minty only displays up to 50 tickets, due to a limitation with the JIRA Rest API. I will deal with this later.


Watch: Vindman corrects Devin Nunes: 'It's Lt. Col. Vindman, please' Army Lt. Col. Alexander Vindman, director on the National Security Council, takes issue with Congressman Devin Nunes. 19 Nov 2019, 23:28


|
QuantLib: a free/open-source library for quantitative finance
Fully annotated sources - version 1.30
|
Multi curve sensitivities. More...
#include <ql/experimental/termstructures/multicurvesensitivities.hpp>
Public Member Functions | |
| MultiCurveSensitivities (curvespec curves) | |
| Multi curve sensitivties. More... | |
| Matrix | sensitivities () const |
| Matrix | inverseSensitivities () const |
| std::vector< std::string > | headers () const |
| Public Member Functions inherited from LazyObject | |
| ~LazyObject () override=default | |
| void | update () override |
| void | forwardFirstNotificationOnly () |
| void | alwaysForwardNotifications () |
| void | recalculate () |
| void | freeze () |
| void | unfreeze () |
| Public Member Functions inherited from Observable | |
| Observable () | |
| Observable (const Observable &) | |
| Observable & | operator= (const Observable &) |
| virtual | ~Observable ()=default |
| void | notifyObservers () |
| Public Member Functions inherited from Observer | |
| Observer ()=default | |
| Observer (const Observer &) | |
| Observer & | operator= (const Observer &) |
| virtual | ~Observer () |
| std::pair< iterator, bool > | registerWith (const ext::shared_ptr< Observable > &) |
| void | registerWithObservables (const ext::shared_ptr< Observer > &) |
| Size | unregisterWith (const ext::shared_ptr< Observable > &) |
| void | unregisterWithAll () |
| virtual void | update ()=0 |
| virtual void | deepUpdate () |
Private Types | |
| typedef std::map< std::string, Handle< YieldTermStructure > > | curvespec |
LazyObject interface | |
| std::vector< Rate > | origZeros_ |
| std::vector< Handle< Quote > > | allQuotes_ |
| std::vector< std::pair< Date, Real > > | origNodes_ |
| Matrix | sensi_ |
| Matrix | invSensi_ |
| curvespec | curves_ |
| std::vector< std::string > | headers_ |
| void | performCalculations () const override |
| std::vector< Real > | allZeros () const |
| std::vector< std::pair< Date, Real > > | allNodes () const |
Additional Inherited Members | |
| Public Types inherited from Observer | |
| typedef set_type::iterator | iterator |
| Protected Member Functions inherited from LazyObject | |
| virtual void | calculate () const |
| Protected Attributes inherited from LazyObject | |
| bool | calculated_ = false |
| bool | frozen_ = false |
| bool | alwaysForward_ = true |
Multi curve sensitivities.
This class provides a simple (yet most likely not the fastest) way to create sensitivities to the par quotes, provided in the piecewiseyieldcurve for stripping. If constructed with more than one curve, the class iterates over all quotes of the provided curves and shifts each quote of all provided curves taking interdependence into account.
The class computes the sensitvities as a QuantLib Matrix class in the form:
\[ \frac{\partial z_i}{\partial q_j} \]
where the \((z_i)_{i\in \{1,...,n\}}\)'s are the implied values (being the traits used during curve constructions, e.g. ZeroYield, Discountfactors or ForwardRates) and the the \((q_i)_{i\in \{1,...,n\}}\)'s are the quoted par rates.
\xrefitem deprecated "Deprecated" "Deprecated Features" To be removed as unused. Copy it in your codebase if you need it. Deprecated in version 1.27.
Definition at line 64 of file multicurvesensitivities.hpp.
|
private |
Definition at line 66 of file multicurvesensitivities.hpp.
|
explicit |
Multi curve sensitivties.
| curves | std::map of string (curve name) and handle to piecewiseyieldcurve |
Definition at line 73 of file multicurvesensitivities.hpp.
| Matrix sensitivities | ( | ) | const |
Definition at line 134 of file multicurvesensitivities.hpp.
| Matrix inverseSensitivities | ( | ) | const |
Definition at line 139 of file multicurvesensitivities.hpp.
| std::vector< std::string > headers | ( | ) | const |
Definition at line 92 of file multicurvesensitivities.hpp.
|
overrideprivatevirtual |
This method must implement any calculations which must be (re)done in order to calculate the desired results.
Implements LazyObject.
Definition at line 110 of file multicurvesensitivities.hpp.
|
private |
Definition at line 158 of file multicurvesensitivities.hpp.
Definition at line 144 of file multicurvesensitivities.hpp.
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mutableprivate |
Definition at line 102 of file multicurvesensitivities.hpp.
Definition at line 103 of file multicurvesensitivities.hpp.
Definition at line 104 of file multicurvesensitivities.hpp.
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mutableprivate |
Definition at line 105 of file multicurvesensitivities.hpp.
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private |
Definition at line 105 of file multicurvesensitivities.hpp.
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private |
Definition at line 106 of file multicurvesensitivities.hpp.
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Definition at line 107 of file multicurvesensitivities.hpp.


func printNumberOfRooms() {
print("The number of rooms is \(numberOfRooms)")
}
if john.residence?.printNumberOfRooms() != nil {
print("It was possible to print the number of rooms.")
} else {
print("It was not possible to print the number of rooms.")
}
// Prints "It was not possible to print the number of rooms."
if (john.residence?.address = someAddress) != nil {
print("It was possible to set the address.")
} else {
print("It was not possible to set the address.")
}
// Prints "It was not possible to set the address."


This project was done on my first day of class! It is a basic website with some information about me, my family and some pets. (Actually I worked on it before class officially started)
Programming languages used
This project was done on 01/08/19 my second day of class. It was a fun project I worked on with Ryan Dikeos. We praticed branching with this project I styled the 'sane' branch and Ryan styled the 'insane' branch.
Programming languages used
This project was done on 01/10/19 it was a very interesting project as it introduced me to bootstrap.css. Jared Farkas was my partner and he understood the concepts very quickly and we were able to make a pretty decent replica of the Wikipedia home page. Its held together mostly by duct tape and hope though, if you make the viewport small enough it totally messes up and the "not logged in" part doesn't scale with the rest of the page. But we are still learning about good page design.
Programming languages used


--- layout: post authors: ["Amy Brown"] title: "Introducing Arliss Collins" date: 2014-01-06 time: "14:30:00" tags: ["Administration"] ---
The incredible expansion of Software Carpentry over the last few months has been exciting and a little overwhelming, so we're pleased to introduce the newest member of the Mozilla Science Lab team, Arliss Collins. Arliss's background is in engineering and project management, and she'll be working with me, Kaitlin and Greg on bootcamp management, infrastructure development and maintenance, and communications.


| Total net profit | -17.06 | Gross profit | 21.56 | Gross loss | -38.62 |
| Profit factor | 0.56 | Expected payoff | -0.81 | ||
| Absolute drawdown | -32.08 | Maximal drawdown (%) | 31.24 (-3719.05%) | ||
| Total trades | 21 | Profit trades (% of total) | 6 (28.57%) | Loss trades (% of total) | 15 (71.43%) |
| Long positions (won %) | 15 (26.67%) | Short positions (won %) | 6 (33.33%) | ||
| Largest | profit trade | 15.02 | loss trade | -6.03 | |
| Average | profit trade | 3.59 | loss trade | -2.57 | |
| Minimum | profit trade | 0.12 | loss trade | -0.84 | |
| Maximum | take profit in pips | 195.2 | stop loss in pips | -154.4 | |
| Average | take profit in pips | 143.7 | stop loss in pips | -124.8 | |
| Minimum | take profit in pips | 114.1 | stop loss in pips | -73.3 | |
| Maximum | Reward-Risk | 2.7 | Duration in minutes | 8012 | |
| Average | Reward-Risk | 1.2 | Duration in minutes | 2448.1 | |
| Minimum | Reward-Risk | 0.7 | Duration in minutes | 64 | |
| Maximum | consecutive wins (profit in money) | 4.0 (6.42) | consecutive losses (loss in money) | 7.0 (-13.96) | |
| Average | consecutive wins | 2.0 | consecutive losses | 5.0 |
| Total net profit | -17.73 | Gross profit | 6.42 | Gross loss | -24.15 |
| Profit factor | 0.27 | Expected payoff | -1.18 | ||
| Absolute drawdown | -17.73 | Maximal drawdown (%) | 16.89 (-2010.71%) | ||
| Total trades | 15 | Profit trades (% of total) | 4 (26.67%) | Loss trades (% of total) | 11 (73.33%) |
| Largest | profit trade | 1.88 | loss trade | -3.2 | |
| Average | profit trade | 1.6 | loss trade | -2.2 | |
| Minimum | profit trade | 1.24 | loss trade | -0.84 | |
| Maximum | take profit in pips | 195.2 | stop loss in pips | -154.4 | |
| Average | take profit in pips | 143.6 | stop loss in pips | -124.9 | |
| Minimum | take profit in pips | 114.1 | stop loss in pips | -73.3 | |
| Maximum | Reward-Risk | 2.7 | Duration in minutes | 8012.0 | |
| Average | Reward-Risk | 1.2 | Duration in minutes | 2799.1 | |
| Minimum | Reward-Risk | 0.7 | Duration in minutes | 344.0 | |
| Maximum | consecutive wins (profit in money) | 4.0 (6.42) | consecutive losses (loss in money) | 8.0 (-15.61) | |
| Average | consecutive wins | 4.0 | consecutive losses | 8.0 |
| Total net profit | 0.67 | Gross profit | 15.14 | Gross loss | -14.47 |
| Profit factor | 1.05 | Expected payoff | 0.11 | ||
| Absolute drawdown | -14.35 | Maximal drawdown (%) | 14.47 (12058.33%) | ||
| Total trades | 6 | Profit trades (% of total) | 2 (33.33%) | Loss trades (% of total) | 4 (66.67%) |
| Largest | profit trade | 15.02 | loss trade | -6.03 | |
| Average | profit trade | 7.57 | loss trade | -3.62 | |
| Minimum | profit trade | 0.12 | loss trade | -2.49 | |
| Maximum | take profit in pips | 175.8 | stop loss in pips | -147.2 | |
| Average | take profit in pips | 143.8 | stop loss in pips | -124.8 | |
| Minimum | take profit in pips | 121.3 | stop loss in pips | -92.7 | |
| Maximum | Reward-Risk | 1.9 | Duration in minutes | 3343.0 | |
| Average | Reward-Risk | 1.2 | Duration in minutes | 1570.5 | |
| Minimum | Reward-Risk | Duration in minutes | 64.0 | ||
| Maximum | consecutive wins (profit in money) | 1.0 (0.12) | consecutive losses (loss in money) | 4.0 (-14.47) | |
| Average | consecutive wins | 1.0 | consecutive losses | 4.0 |


| Folders | |||
| <parent> |
| File | File | File | File | File | File | File | File | File | File |
|---|---|---|---|---|---|---|---|---|---|
| BinarayTr...l.java | CutOffTre...t.java | Matrix.java | OpenTheLock.java | RaceCar.java | RottingOr...s.java | SlidingPuzzle.java | WallsAndGates.java | WordLadder.java | WordLadderII.java |
| BusRoutes.java |
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{% extends "_base.htm" %} {% block bodycontent %}
This page provides demonstrations of a couple functions of boto. In theory any MTurk operation can be achieved by calling different functions. The documentation for the MTurk Operations and their implementations in Boto are available from the following sites: Boto MTurk.
Before you can access your MTurk account, you must obtain your Amazon Web Service (AWS) access_key aka AWS_ACCESS_KEY_ID and your secret_key aka AWS_SECRET_ACCESS_KEY. This is like your user name and password. These need to be put into the app.yaml file as environment variables. Information for how to find these credentials can be found here.
Amazon has built a 'sandbox' that allows developers to test code in a test environment. Any code executed in this environment does not actually withdraw fees from the MTurk account and MTurk workers will not accept the hits. Once you are ready to publish the site, all you need to do is change one line of code in /boto/connection.py line 950 to use the production site instead of the sandbox.
This code retrieves the account balance from MTurk. If the sandbox is being used, it should always display a balance of $10,000.
Account Balance: {{AccountBalance}}
{% endblock %}