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How swappiness kernel affects the compression performance
How swappiness kernel affects the compression performance
The swappiness parameter was modified in order to see performance improvement when compressing a file.
Dennis Kingston / Alberto Hernández
Microlattice: Un  material innovador
Microlattice: Un material innovador
Microlattice
Andrea Pinzon, Mariana Gil y Angela Pinto
Difficulty Adjustment Algorithms in Cryptocurrency Protocols
Difficulty Adjustment Algorithms in Cryptocurrency Protocols
As of this writing, the algorithm employed for difficulty adjustment in the CryptoNote reference code is known by the Monero Research Lab to be flawed. We describe and illustrate the nature of the flaw and recommend a solution. By dishonestly reporting timestamps, attackers can gain disproportionate control over network difficulty. We verify this route of attack by auditing the CryptoNote reference difficulty adjustment code, which, we reimplement in the Python programming language. We use a stochastic model of blockchain growth to test the CryptoNote reference difficulty formula against the more traditional Bitcoin difficulty formula. This allows us to test our difficulty formula against various hash rate scenarios. This research bulletin has not undergone peer review, and reflects only the results of internal investigation.
Surae Noether
DEVOLOPMENT OF AN ALGORITHM TO DETERMINATE THE ORIENTATION USING AN IMU
DEVOLOPMENT OF AN ALGORITHM TO DETERMINATE THE ORIENTATION USING AN IMU
In this paper we will study an algorithm designed by Madgwick which is commonly used to determine the orientation of a quadcopter. The algorithm uses a group of accelerometers, gyroscopes and magnetometers integrated in what is called an IMU as input. Some differences have been found between the results obtained by the original paper and the implementation done by the author. Therefore, a thorough study has been made, finding a miscalculation in the equations. The results show a relative average error in the orientation of 1,44 ppm.
andoni
Analysis Of Using Social Network On Higher Education
Analysis Of Using Social Network On Higher Education
The Internet has become the broadest area in which to exchange information and communicate.Some use this function in a positive way, whilst others do so negatively. With the growth of the Internet, social networks have also grown. Social networks are used in different fields and for different proposes. They are used in higher education to enhance training and collaborative learning and exchange knowledge in an interaction environment. This paper aims at finding the 10 best universities by measuring the use of social networks in education.Universities are selected for this experiment from the Academic Influence Ranking website for the domain of computer science overall (type A) (for more information about the selected universities please visit this link: http://pubstat.org/).
fatimah
The Hopf Fibration: Homotopy Groups of Spheres
The Hopf Fibration: Homotopy Groups of Spheres
Created for Stanford Mathematics Camp 2016. A brief introduction to the Hopf Fibration for introductory topology students.
Trey Connelly
Path Integrals an Introduction
Path Integrals an Introduction
Here we discuss the path integral formalism for quantization of fields. The basic idea is reviewed and explained. This is completely based on the book ``Quantum Field Theory A Modern Introduction" by Michio Kaku. For calculation natural system of units is taken.
manosh.t.m
The addition formulas for the hyperbolic sine and cosine functions via linear algebra
The addition formulas for the hyperbolic sine and cosine functions via linear algebra
We present a geometric proof of the addition formulas for the hyperbolic sine and cosine functions, using elementary properties of linear transformations.
David Radcliffe
Methods to prevent overfitting and solve ill-posed problems in statistics: Ridge Regression and LASSO
Methods to prevent overfitting and solve ill-posed problems in statistics: Ridge Regression and LASSO
Linear regression is one of the most widely used statistical methods available today. It is used by data analysts and students in almost every discipline. However, for the standard ordinary least squares method, there are several strong assumptions made about data that is often not true in real world data sets. This can cause numerous problems in the least squares model. One of the most common issues is a model overfitting the data. Ridge Regression and LASSO are two methods used to create a better and more accurate model. I will discuss how overfitting arises in least squares models and the reasoning for using Ridge Regression and LASSO include analysis of real world example data and compare these methods with OLS and each other to further infer the benefits and drawbacks of each method.
Chris Van Dusen

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