[3] **viXra:1406.0124 [pdf]**
*replaced on 2014-09-27 23:38:47*

**Authors:** Laszlo B. Kish, Claes-Goran Granqvist

**Comments:** 9 Pages. Accepted for publication in Entropy (open access)

We introduce the so far most efficient attack against the Kirchhoff-law–Johnson-noise (KLJN) secure key exchange system. This attack utilizes the lack of exact thermal equilibrium in practical applications and is based on cable resistance losses and the fact that the Second Law of Thermodynamics cannot provide full security when such losses are present. The new attack does not challenge the unconditional security of the KLJN scheme, but it puts more stringent demands on the security/privacy enhancing protocol than for any earlier attack. In this paper we present a simple defense protocol to fully eliminate this new attack by increasing the noise-temperature at the side of the smaller resistance value over the noise-temperature at the at the side with the greater resistance. It is shown that this simple protocol totally removes Eve’s information not only for the new attack but also for the old Bergou-Scheuer-Yariv attack. The presently most efficient attacks against the KLJN scheme are thereby completely nullified.

**Category:** Data Structures and Algorithms

[2] **viXra:1406.0105 [pdf]**
*submitted on 2014-06-16 18:39:38*

**Authors:** Michail Zak

**Comments:** 46 Pages.

One of the fundamental objectives of mathematical modeling is to interpret past and present, and, based upon this interpretation, to predict future. The use at time t of available observations from a time series to forecast its value at some future time t+l can provide basis for 1) model reconstruction, 2) model verification, 3) anomaly detection, 4) data monitoring, 5) adjustment of the underlying physical process. Forecast is usually needed over a period known as the lead time that is problem specific. For instance, the lead time can be associated with the period during which training data are available. The accuracy of the forecast may be expressed by calculating probability limits on either side of each forecast. These limits may be calculated for any convenient set of probabilities, for example, 50% and 90%. They are such that the realized value of the time series, when it eventually occurs, will be included within these limits with the stated probability.

**Category:** Data Structures and Algorithms

[1] **viXra:1406.0044 [pdf]**
*replaced on 2014-09-13 19:56:25*

**Authors:** Morio Kikuchi

**Comments:** 129 Pages.

We fill a plane up regularly using painting algorithms(2).

**Category:** Data Structures and Algorithms