Alireza Manavi

One of the major problems we have, in simulation of the interacting fermionic system, using brute force methods like quantum Monte Carlo, is the fermionic sign problem. This problem arises while working with highly oscillatory functions. in this paper, we have described the problem and used deep neural network to predict sign of each configurations partition function. At last we have compared the prediction power of three different neural network designs and studied the scalability of each model.

The main question of this article is about whether cryptocurrencies, within their decentralization aspects, are a real commodity or/and a virtual currency. To resolve such a dilemma, we compare 7 cryptocurrencies with a sample of the three types of monetary systems: 28 fiat money, 2 commodities, 2 commodity based indices, and 3 financial market indices. We use the matrix correlation method. We display dendrograms and observe “hierarchy clustering”, as a function of data coarse graining. In fact, we confirm that the cryptocurrencies are not decentralized. We observe also that most of the currencies in the world are not significantly correlated or present a weak correlation with cryptocurrencies. Our results show that the cryptocurrency market and Forex market belong to different system communities (or regions).

This paper aims to find the maximum speed of an ejected steel ball from a magnetic cannon and determine its relation to other setup parameters. A magnet induces dipole moments in steel balls inside a magnetic cannon and makes them stick together along a straight line. At the moment of collision, this attraction reduces the motion of the balls in other directions.