Page 1 of 2 1 2 Last Jump to page: Results 1 to 10 of Thread: Lore behind Burning Wall. Lore behind Burning Wall So I might have missed it in my search but is there a place in game that explains how Burning Wall came to be? It's a crash site from a chunk of Dalamud, hence the over-aspected crystalline aether monstrosity and mirrorknights.
According to one of the main scenario quests, a lot of the over-aspected crystal in the area is wind aspected, but in a chaotic state. The area was actually an aetherial gate in Version 1.
Dalamud fragments already make the aether behave strangely; drop a giant chunk of it on an aetherial gate I guess the Burning Wall is what happens? If you go exploring at The Burning Wall sometime, look around closely - there's hidden fragments of the Allag tech hanging around X30Y27, for one An entrance to something someday, perhaps Thanks, I was assuming that it was caused by a chunk of Dalamud, but couldn't confirm it, since none of the other chunks La Nocea, Shroud caused something like the burning Wall.
Atherial gate explosion makes sense. Yeah, the 1. My guess is it is meant to have some connection to other metaphors for fire and the sun that are found all throughout Thanalan such as Azeyma's glyph on the ancient Bel'hadian ruins like the Invisible City, and the Red Labyrinth in Southern Thanalanpossible given the fact fire is an important part of both Ul'dahn and amalj'aa culture.
Originally Posted by Rannie. Aaaaannnd now I just had a mental image of Lahabrea walking into a store called Bodies R Us and trying on different humans Juuuuuust right. Pardon me Burning Wall is the place that Golden Fleece is living?
Originally Posted by Altimis. Originally Posted by Darske. Yes, that's the place. Interestingly enough is the Allag tech you can catch glimpses of.Bjarni made everything perfect. There was no rushing for trains, boats or busses. Everything was perfectly timed so that we did not feel like we had to hurym but we did not have to wait long periods of time to get to our next destination.
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Automotive was not included in our survey, but sales data from the Traffic Management Bureau of the Ministry of Public Security in China suggest significant trading up. In 2011, 51 percent of the renminbi spent on cars by Chinese consumers were for autos cheaper than 100,000 RMB.
These sales accounted for only 43 percent of the market. Cars selling for 100,000 to 250,000 RMB grew twice as fast with a compound annual growth rate (CAGR) of 19 percent versus 9 percent. And cars with price tags between 250,000 and 400,000 RMB grew the fastest of all, with 23 percent CAGR. In 2011, we observed a big generational difference between consumers in their late 50s and early 60s, who were very conservative spenders, and all of the age cohorts younger than them.
We predicted that by 2020, as the needs of consumers over the age of 55 changed along with their economic confidence, their spending habits would follow suit, making this age group worth pursuing by consumer-product companies. If anything, we underestimated the speed and force with which this trend would unfold. For example, 52 percent of the people in this age group showed a preference for premium products, compared to just 32 percent in 2012.
They leaped from being the most conservative age group to the one most likely to trade up. Similarly, the preference for famous brand names among these older buyers jumped by more than 20 percent, fully closing the previous difference among cohorts. That said, the upper age group has remained more pragmatic and cost conscious than any other age group, as we discuss in the following section.
Back in 2011, even as we were predicting changes in the behavior and preferences of Chinese consumers, we also saw ways in which their essential pragmatism would likely stay the same. For instance, we anticipated that impulse buying would remain lower than in other countries and that value for money would continue to be an important consideration when choosing products and services. Interestingly, Chinese consumers across all age groups have, in some ways, become even more pragmatic. The individual consumer We also predicted that as Chinese consumers aspire to a better life and trade up their purchases, they would become more discerning and gradually more individualistic.
This would lead, for example, to a shift toward more healthy choices, more user-friendly products, and products and brands that better fit their personality. This could be a big opportunity for niche brandsand a threat to the mass-market brands that had won big in previous years by using scale and ubiquitous availability, supported by the trust gained by heavy advertising.
Our latest research certainly shows a decrease in consumption in categories deemed less healthy and a willingness to spend significantly more on health and more environmentally conscious categories.
It also shows consumers are more likely to spend more to indulge themselves and more likely to try new technology. While their consumption choices have become more individualistic, though, it is important to note that family values continue to be at the top of their priorities (Exhibit 3). One area our predictions missed, however, was by anticipating that consumers, as they became more individualistic in their choices, might focus less on basic product reliability and safety.
Perhaps in part because of a number of more recent food scandals, however, consumers seemed more concerned with these issues in 2015 than they were before. When our team first started researching Chinese consumers, nearly ten years ago, many of us were surprised by their fickle attitude toward brands. Fewer than half of consumers tended to stick with their favorite brands, compared, for example, with almost three quarters of US consumers.
As we debated this tendency while making our predictions, we wondered if, in the clash between pragmatism and individualism, brand loyalty would stay low, increase, or even decline.
Ultimately, we decided it would increase as the emotional benefits of brands became more important to consumers and as increased choice and availability of branded products (online and off) would allow consumers to optimize for price and convenience without changing choices too often. Our recent research confirmed the changes we anticipated.Each row represents an instance (or example).
Each column in the file represents a feature or field. The last column usually represents the class or objective field. The file might have a first row named header with a name for each field.
See below for more details. You can also list all of your sources. The first dictionary defines the keys that will be selected. To create a new source, you need to POST the file containing your data to the source base URL.
The file must be attached in the post as a file upload. This allows you to upload binary files in compressed format (. You can easily do this using curl. The option -H lets curl set the content type header while the option -X sets the http method.
You will get the access token and the refresh token. Google Drive example:Select the option to create source from Google Drive: Allow BigML access to your Google Drive: Get the access token and refresh token: After complete these steps you need to POST to the source endpoint URL an object containing at least the file ID (for Google Drive) or the bucket and the file name (for Google Storage) and the access token.
Including also the refresh token is optional before your access token expires. Including it avoids you to be worried about expiration time. You first need to authorize BigML access from your own Google Apps application. After the authorization process you will get your access token and refresh token from the Google Authorization Server.
Then the process is the same as creating a remote source using BigML application described above. You need to POST to the source endpoint an object containing at least the file ID (for Google Drive) or the bucket and the file name (for Google Storage) and the access token, but in this case you will also need to include the app secret and app client from your App.
Again, including the refresh token is optional. Your values for app client and app secret appear as Client secret and Client ID in Google developers console respectively.
This way is specially useful if you want to model small amounts of data generated by an application. It can be compressed, gzipped, or zipped. You can also use curl to customize your new source with a name and different parser. For example, to create a new source named "my source", without a header and with "x" as the only missing token.
However, if you do specify it, BigML. While the handling of numeric, categorical, or items fields within a decision tree framework is fairly straightforward, the handling of text fields can be done in a number of different ways.
At the source level, BigML. In the near future, BigML. For text fields, BigML. If all is selected, then both full terms and tokenized terms are used. For example, datasets containing all products bought by users or prescription datasets where each patient is associated to different treatments.
These datasets are commonly used for Association Discovery to find relationships between different items.
Once a field has been detected as items, BigML tries to automatically detect which is the best separator for your items. During the source pre-scan BigML tries to determine the data type of each field in your file.
For instance, if a field named "date" has been identified as a datetime with format "YYYY-MM-dd", four new fields will be automatically added to the source, namely "date. For each row, these new fields will be filled in automatically by parsing the value of their parent field, "date".
For example, if the latter contains the value "1969-07-14", the autogenerated columns in that row will have the values 1969, 7, 14 and 1 (because that day was Monday). When a field is detected as datetime, BigML tries to determine its format for parsing the values and generate the fields with their components.See the Model Object definition below. Controls whether the frequency of repeated (or very similar) data points lower the anomaly score of the repeats.
In a future version, you will be able to share anomaly detectors with other co-workers or, if desired, make them publicly available. This is the date and time in which the anomaly detector was updated with microsecond precision.
A list of top anomalies objects. A list with the trees representing the anomaly detector. Each tree conforms the Root Object definition. A list of floats with the relative importance for each field. The importances tell us which values contributed most to the anomaly score. A number between 0 and 1.
The closer to one the more anomalous is the row. A status code that reflects the status of the anomaly detector creation. Example: 1 complement optional If complement is true, complementary items are also taken into account. Example: "This is a description of my new association" Global numeric field transformation parameters.
Specifies the fields that won't be included in the association. Example: true name optional The name you want to give to the new association. Example: "lift" seed optional A string to be hashed to generate deterministic samples.
Example: "width" All the information that you need to recreate the association. It includes the field's dictionary describing the fields, and the associations' items and rules. See the Associations Object definition below. This will be 201 upon successful creation of the association and 200 afterwards.
Make sure that you check the code that comes with the status attribute to make sure that the association creation has been completed without errors. This is the date and time in which the association was created with microsecond precision. The list of fields's ids that were excluded to build the association. In a future version, you will be able to share association with other co-workers or, if desired, make them publicly available.
This is the date and time in which the association was updated with microsecond precision. See the discretization table. An array of unique items detected in the datasest. See Item Object below. A real number between -1 and 1 specifying the minimum leverage for the rules discovered. An array of association rules discovered in the dataset.
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