Social Science

2101 Submissions

[3] viXra:2101.0140 [pdf] submitted on 2021-01-22 20:33:58

Нашествие татаро-монголов на Русь. Что это было (Invasion of the Tatar-Mongols in Russia: What Was It)

Authors: Alexander I. Dubiniansky, Ulija A. Krupenina
Comments: 654 Pages. dubinyansky@mail.ru [Corrections made by viXra Admin to conform with the requirements on the Submission Form]

Изложена авторская версия нашествия татаро-монголов на славян. Изложена полная история возникновения нации татаро-монголов. Изложены причины возникновения человеческих уродств.(The author's version of the invasion of the Tatar-Mongols on the Slavs is presented. The complete history of the emergence of the Tatar-Mongol nation is presented. The reasons for the occurrence of human deformities are stated.)
Category: Social Science

[2] viXra:2101.0129 [pdf] replaced on 2021-04-08 20:56:01

An Introduction to the Relearning Cycle Index $i_{rel}$ and the Average Split Function $\bar{f}_{rel}$ of the Source Energy Exploitation by and for the Neural Networks.

Authors: Alexis Zaganidis
Comments: 3 Pages.

We define the relearning cycle index $i_{ReL}$ and the average split function $\bar{f}_{ReL}$ of the source energy exploitation by and for the neural networks. We propose an optimized learning strategy which depend on a fixed relearning cycle index $i_{ReL}$ and a fixed average split function $\bar{f}_{ReL}$. In practice, this theory may explain why the communist politics in Russia and in China faced strong difficulties at the 20th century and why the private companies politics in Western countries faced critical difficulties at the beginning of the 21th century. We conclude with some critical hints for the future relearning cycles of the source energy exploitation by and for the neural networks.
Category: Social Science

[1] viXra:2101.0069 [pdf] submitted on 2021-01-10 18:59:02

Approach to Credit Valuation Adjustment and Wrong Way Risk

Authors: Tim Xiao
Comments: 25 Pages.

This paper presents a Least Square Monte Carlo approach for accurately calculating credit value adjustment (CVA). In contrast to previous studies, the model relies on the probability distribution of a default time/jump rather than the default time itself, as the default time is usually inaccessible. As such, the model can achieve a high order of accuracy with a relatively easy implementation. We find that the valuation of a defaultable derivative is normally determined via backward induction when their payoffs could be positive or negative. Moreover, the model can naturally capture wrong or right way risk.
Category: Social Science