#treebase

[codingprolab]

Com S 228 Project 4: Archived Message Reconstruction solved

1. Problem Description The objective of this exercise is to reconstruct/unzip a message archived with a binary-treebased algorithm. The program should ask for a single filename at the start: “Please enter filename to decode: “, decode the message in the file and print it out to the console. The name of the compressed message file will end in .arch, e.g. “monalisa.arch”. The file consists of two…

[goldenlaurelleaveswrites]

Back to September

18. Lemon Tree

Based on this prompt list

AO3

__________________________________________

She had been the Guardian for ten years now, having taken up the mantle just after she turned fourteen. When she had first taken on the role, she had been rather… green. Inexperienced. 

Completely in over her head. 

No one could blame her. She had been young. Still a child and already with far too much on her shoulders. She had made plenty of mistakes. Some graver than others. But she had learned. She had learned how to take care of the kwamis and guard their secrets. 

And she had learned to ignore their puppy eyes. 

Luka, on the other hand… had seemingly not. 

She frowned at the potted lemon tree she had gotten for her sixteenth birthday. It had still been small then. Only a few inches high. But over the years, it had grown into a beautiful tree perfect for brightening up her room. And when she and Luka had moved in together, it had been perfect for adding a bit of greenery to their living room. 

It was not, however, perfect for the purpose it was currently serving. 

“Star,” she called. 

“Yeah?” He stuck his head out from the bedroom, where he had been unpacking the last of the boxes.  

“Why are there swings hanging from my lemon tree?” 

His eyes darted to the aforementioned tiny swings, which Ziggy and Mullo were perched on, swinging back and forth and shaking the branches. His brows furrowed. "They said they asked you-"

"We did!" 

She levelled Ziggy with another look. "Did they tell you I said no?" 

"I- no. No, they did not." 

"Nobody ever lets us have any fun," Ziggy whined as she jumped off her swing to hover in the air. "I'm sorry, Marinette." 

"Me too," Mullo added, blinking up at her with wide eyes. 

"And?" 

"We're sorry, Luka." 

He sighed. "I'll forgive you. Now, why don't we find somewhere else to put up your swings." Both kwamis immeditaely perked up and zoomed off, chattering to each other about where the best place for their swings would be. She watched them go, rolling her eyes as they disappeared into the kitchen. The weight of his arms settled around his shoulders as he pulled her against him. "I'm sorry; I didn't know they had asked you too," he murmured as he rested his chin on her head. 

She leaned back into him and hummed in response. 

"They really pulled the wool over my eyes, didn't they?"

"They sure did. But don't worry, you'll get used to living with kwamis. Eventually."

[themountainkings]

10.) Flowing #inktober #inktober2018 #themountainkings #tienchiart #artistsoninstagram #sketchbook #pen #ink #drawing #art #design #fantasy #fun #flowing #treebase #elves https://www.instagram.com/p/BoxZaGJg1JA/?utm_source=ig_tumblr_share&igshid=9utll5x5khei

[irispublishersengineeringscience]

Iris Publishers - Global Journal of Engineering Sciences (GJES)

The Acceleration of Least Squares Monte Carlo in Risk Management

Authored   by   Lu Xiong

The Application of LSMC in Risk Management

Solvency Capital Requirement (SCR) of Solvency II requires the computation of the economic capital, the minimum capital giving the insurance company a 99.5% survival probability over a oneyear horizon via a full probability distribution forecast [9,10].

The SCR at level α=99.5% can be computed as

Distributed Regression for LSMC Speedup

When it comes to multi-factor risks modeling approximation, the multi-dimensional polynomial would be extremely complicated. This would make the regression slow or not possible to finish within reasonable time.

To over the computational complexity of multi-risk factor LSMC, we propose distributed regression for LSMC. The idea of distributed regression is fairly simple: instead of running the regression on one computer, we distribute the regression task to multiple computers (usually using cloud computers), then average the regressed coefficients to get the final regression equation. In this way the computing time can be significantly reduced. We can mathematically prove this simple idea can actually obtain the optimal regression results [12].

There are several advantages of distributed regression: First, the computing time for the traditional least square regression is O(n3), where n is number of observations in data. While for distributed regression, it’s O(n3/m2), where m is the number of distributed computers. If we distributed the regression task to 10 computers, we could reduce to computing time to 1% of the original regression, 50 computers to 0.04%. Second, distributed regression can protect the data privacy, because very little or no communication is required when computing from distributed computers. Therefore, almost no data exchanged happened between different data platforms. If we have policy data stored in different platforms and we don’t want to share the data across, we can use distributed regression to obtain the regression coefficients from each platform then average the coefficients to get the total regression equation.

There are several advantages using distributed regression to accelerate the LSMC. 1) The current parallel algorithms for LSMC require the parallel computing of the big matrix inverse, while using distributed regression we only need compute the small matrix inversion for each chunk of data. 2) When comes to multi-risk modeling, the amount of the outer scenarios would be huge that no single computer can handle it. For a N risk-factor problem, it will require 10000N outer scenarios if we simulate 10,000 outer scenarios for each risk-factor. If we use distributed regression, each computer only needs processes a smaller chunk of data assigned. 3) This divide-and-conquer type distributed learning method can also be applied to speed up other algorithms like clustering, treebased method, deep learning etc. 4) Easy to be scaled on distributed framework like Map-reduce, or Spark [13].

 For more about Iris Publishers please click on: http://irispublishersgroup.com/submit-manuscript.php.

 To read more about this article- https://irispublishers.com/gjes/fulltext/the-acceleration-of-least-squares-monte-carlo-in-risk-management.ID.000657.php

 Indexing List of Iris Publishers: https://medium.com/@irispublishers/what-is-the-indexing-list-of-iris-publishers-4ace353e4eee

 Iris publishers google scholar citations :https://scholar.google.co.in/scholar?hl=en&as_sdt=0%2C5&q=irispublishers&btnG=

[inspireif]

💦Today's Madness..... The"DabHead" is High as A Kite!. Working on a new one with #treebaseklear !. Print and Pin set..!!#thechoiceisclear#treebase#dab#wax#oil#710#california#theseventhletter#waisted#2shae

[twystedrootstrees]

It's nice to have a flat surface for a change. Normally this stage involves rubber bands and willpower! Happy 1st of December 💖 . . . . #twystedroots #treebase #clamps #e6000 #agateslice #smallbizsaturday #gettingcreative #firstofthemonth https://www.instagram.com/p/Bq2MVw-lPMx/?utm_source=ig_tumblr_share&igshid=1kbvdqbha2ii7

[cannabisartist]

“New Industry Standard” 🌴 @treebaseklear 🌴 X @str8organics 👨🏻‍🔬 Flavor Terpene Profile: Str8 Lemonade 🍋 #TreeBaseKlear #ThinkKlear #TreeBase #NewIndustryStandard #Str8Organics #Str8Organic https://www.instagram.com/p/BqiMXEgh8z6/?utm_source=ig_tumblr_share&igshid=pagp3s68sau8