150 research outputs found
Using frequency analysis and Grover's algorithm to implement known ciphertext attack on symmetric ciphers
In this paper we construct quantum circuit implementing known ciphertext attack on symmetric cipher. We assume that plaintext is in natural language and have known letter distribution. Our method allows to find key using one query to (quantum) decryption oracle and has O(β{pipe}K{pipe}) time complexity, where K-set of possible keys. Β© 2013 Pleiades Publishing, Ltd
From graphs to keyed quantum hash functions
Β© 2016, Pleiades Publishing, Ltd.We present two new constructions of quantum hash functions: the first based on expander graphs and the second based on extractor functions and estimate the amount of randomness that is needed to construct them. We also propose a keyed quantum hash function based on extractor function that can be used in quantum message authentication codes and assess its security in a limited attacker model
Physics discovery in nanoplasmonic systems via autonomous experiments in Scanning Transmission Electron Microscopy
Physics-driven discovery in an autonomous experiment has emerged as a dream
application of machine learning in physical sciences. Here we develop and
experimentally implement a deep kernel learning workflow combining the
correlative prediction of the target functional response and its uncertainty
from the structure, and physics-based selection of acquisition function, which
autonomously guides the navigation of the image space. Compared to classical
Bayesian optimization methods, this approach allows to capture the complex
spatial features present in the images of realistic materials, and dynamically
learn structure-property relationships. In combination with the flexible
scalarizer function that allows to ascribe the degree of physical interest to
predicted spectra, this enables physical discovery in automated experiment.
Here, this approach is illustrated for nanoplasmonic studies of nanoparticles
and experimentally implemented in a truly autonomous fashion for bulk- and edge
plasmon discovery in MnPS3, a lesser-known beam-sensitive layered 2D material.
This approach is universal, can be directly used as-is with any specimen, and
is expected to be applicable to any probe-based microscopic techniques
including other STEM modalities, Scanning Probe Microscopies, chemical, and
optical imaging
New automata definition of language for game development
Β© 2016 Taylor & Francis Group, London.In this paper, we describe a novel Domain-Specific Language (DSL), which is useful for describing AI in games. This DSL is based on a firm theoretical ground of finite automata theory. We provide full specification of the language and discuss the optimized implementation. We use this DSL in the development of an educational game βBolgar XIVβ
Minimizing collisions for quantum hashing
Β© Medwell Journals, 2017.Hashing is a widely used technique in computer science. The recently proposed quantum hashing has also proved its usefulness in a number of applications. The key property of both classical and quantum hashing is the ability to withstand collisions however, the notion of collision itself is different in the classical and quantum setting. In this study we analyze the set of numeric parameters that determine the probability of quantum collisions for the quantum hashing. Although, there is a general method of obtaining good hashing parameters, it makes sense for comparatively large inputs. That is why we construct different methods to complement the general one. We present two explicit optimization algorithms for computation of quantum hashing parameters: one is based on the genetic approach and the other uses the annealing simulation. The solution to the considered optimization problem can be used for the variety of quantum hash functions and also provides a solution to the general problem of constructing sets of pairwise distinguishable states in low-dimensional spaces
Nitrided Ferroalloy Production By Metallurgical SHS Process: Scientific Foundations and Technology
The main objective of this paper is to present results of the research in the development of a specialized self-propagating high-temperature synthesis (SHS) technology for ferroalloy composites, as applied to steelmaking. The problem of creating such a production cycle has been solved by developing a new approach to the practical implementation of self-propagating high-temperature synthesis, as applied to metallurgy. The metallurgical variation of SHS is based on the use of different metallurgic alloys (including waste in the form of dust from ferroalloy production) as basic raw materials in the new process. Here, the process of synthesis by combustion is realized through exothermic exchange reactions. The process produces a composite, based on inorganic compositions with a bond of iron and/or alloy based on iron. It has been shown that in terms of the aggregate state of initial reagents, metallurgical SHS processes are either gasless or gas-absorbing. Combustion regimes significantly differ when realized in practice. To organize the metallurgical SHS process in weakly exothermic systems, different variations of the thermal trimming principle are used. In the present study, self-propagating high-temperature synthesis of ferrovanadium nitride, ferrochromium nitride and ferrosilicon nitride; which is widely used in steel alloying, was investigated.
Keywords: self-propagating high-temperature synthesis (SHS); composite ferroalloys; nitrides; borides; filtration combustion; ferrovanadium nitride ferrochromium nitride and ferrosilicon nitrid
Dynamic Geometry Environments as a Tool for Computer Modeling in the System of Modern Mathematics Education
Abstract. This paper discusses a number of issues and problems associated with the use of computer models in the study of geometry in university, as well as school mathematics in order to improve its efficiency. We show that one of the efficient ways to solve a number of problems in nowadays mathematics education is to use dynamic geometry environment GeoGebra. We also provide some examples of computer models created with GeoGebra. Keywords: dynamic geometry environment; GeoGebra, computer mathematics; innovations; learning process; interactive computer models; Internet; mathematics education. ΠΠ²Π΅Π΄Π΅Π½ΠΈΠ΅. Π‘ΠΈΡΡΠ΅ΠΌΡ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΠΈ (Π‘ΠΠ), ΠΈΠ»ΠΈ ΠΈΠ½ΡΠ΅ΡΠ°ΠΊΡΠΈΠ²Π½ΡΠ΅ Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠΈΡΡΠ΅ΠΌΡ (ΠΠΠ‘), ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΡΡ ΡΠΎΠ±ΠΎΠΉ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΡΠ΅ ΡΡΠ΅Π΄Ρ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠ΅ ΡΠΎΠ·Π΄Π°Π²Π°ΡΡ ΠΈ ΠΌΠ°Π½ΠΈΠΏΡΠ»ΠΈΡΠΎΠ²Π°ΡΡ Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡΠΌΠΈ, ΠΏΡΠ΅ΠΆΠ΄Π΅ Π²ΡΠ΅Π³ΠΎ Π½Π° ΠΏΠ»ΠΎΡΠΊΠΎΡΡΠΈ (Π² ΠΏΠ»ΠΎΡΠΊΠΎΠΉ ΠΠ²ΠΊΠ»ΠΈΠ΄ΠΎΠ²ΠΎΠΉ Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΠΈ) Π‘ΠΠ ΠΏΡΠ΅Π΄Π½Π°Π·Π½Π°ΡΠ΅Π½Ρ ΠΏΡΠ΅ΠΆΠ΄Π΅ Π²ΡΠ΅Π³ΠΎ Π΄Π»Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π·Π°Π΄Π°Ρ ΡΠΊΠΎΠ»ΡΠ½ΠΎΠ³ΠΎ ΠΊΡΡΡΠ° Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΠΈ: Π² Π½ΠΈΡ
ΠΌΠΎΠΆΠ½ΠΎ ΡΠΎΠ·Π΄Π°Π²Π°ΡΡ Π²ΡΠ΅Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΠ΅ ΠΊΠΎΠ½ΡΡΡΡΠΊΡΠΈΠΈ ΠΈΠ· ΡΠΎΡΠ΅ΠΊ, Π²Π΅ΠΊΡΠΎΡΠΎΠ², ΠΎΡΡΠ΅Π·ΠΊΠΎΠ², ΠΏΡΡΠΌΡΡ
; ΡΡΡΠΎΠΈΡΡ Π³ΡΠ°ΡΠΈΠΊΠΈ ΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΡΠ½ΡΡ
ΡΡΠ½ΠΊΡΠΈΠΉ, ΠΊΠΎΡΠΎΡΡΠ΅ ΡΠ°ΠΊΠΆΠ΅ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈ ΠΈΠ·ΠΌΠ΅Π½ΡΡΡ Π²Π°ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π½Π΅ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ° (ΠΈΠ»ΠΈ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΈΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ²), Π²Ρ
ΠΎΠ΄ΡΡΠ΅Π³ΠΎ(ΠΈΡ
) Π² ΡΡΠ°Π²Π½Π΅Π½ΠΈΠ΅; ΡΡΡΠΎΠΈΡΡ ΠΏΠ΅ΡΠΏΠ΅Π½Π΄ΠΈΠΊΡΠ»ΡΡΠ½ΡΠ΅ ΠΈ ΠΏΠ°ΡΠ°Π»Π»Π΅Π»ΡΠ½ΡΠ΅ Π·Π°Π΄Π°Π½Π½ΠΎΠΉ ΠΏΡΡΠΌΠΎΠΉ Π»ΠΈΠ½ΠΈΠΈ, ΡΠ΅ΡΠ΅Π΄ΠΈΠ½Π½ΡΠ΅ ΠΏΠ΅ΡΠΏΠ΅Π½Π΄ΠΈΠΊΡΠ»ΡΡΡ, Π±ΠΈΡΡΠ΅ΠΊΡΡΠΈΡΡ ΡΠ³Π»ΠΎΠ², ΠΊΠ°ΡΠ°ΡΠ΅Π»ΡΠ½ΡΠ΅; ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΡΡ Π΄Π»ΠΈΠ½Ρ ΠΎΡΡΠ΅Π·ΠΊΠΎΠ², ΠΏΠ»ΠΎΡΠ°Π΄ΠΈ ΠΌΠ½ΠΎΠ³ΠΎΡΠ³ΠΎΠ»ΡΠ½ΠΈΠΊΠΎΠ² ΠΈ Π·Π°ΠΌΠΊΠ½ΡΡΡΡ
ΠΊΡΠΈΠ²ΡΡ
ΠΈ Ρ.Π΄. ΠΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, Π² Π½Π΅ΠΊΠΎΡΠΎΡΡΡ
Π‘ΠΠ ΠΊΠΎΠΎΡΠ΄ΠΈΠ½Π°ΡΡ ΡΠΎΡΠ΅ΠΊ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ Π²Π²Π΅Π΄Π΅Π½Ρ Π²ΡΡΡΠ½ΡΡ Π½Π° ΠΏΠ°Π½Π΅Π»ΠΈ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ², Π° ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ ΠΊΡΠΈΠ²ΡΡ
ΠΈ ΠΊΠ°ΡΠ°ΡΠ΅Π»ΡΠ½ΡΡ
ΠΊ Π½ΠΈΠΌ Π² ΡΡΡΠΎΠΊΠ΅ Π²Π²ΠΎΠ΄Π° ΠΏΡΠΈ ΠΏΠΎΠΌΠΎΡΠΈ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΡ
ΠΊΠΎΠΌΠ°Π½Π΄. Π‘ΠΠ ΡΠ°ΠΊΠΆΠ΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡ ΡΠ°Π±ΠΎΡΠ°ΡΡ ΠΈ Ρ Π±ΠΎΠ»Π΅Π΅ ΡΠ»ΠΎΠΆΠ½ΡΠΌΠΈ Π΄Π»Ρ ΠΏΠΎΠ½ΠΈΠΌΠ°Π½ΠΈΡ ΡΡΡΠ΄Π΅Π½ΡΠ° ΡΠ°Π·Π΄Π΅Π»Π°ΠΌΠΈ Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΠΈ: ΠΏΡΠΎΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠΉ [2] ΠΈ Π΄ΠΈΡΡΠ΅ΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ. ΠΡΠΎΠ±ΡΡ ΡΠ΅Π½Π½ΠΎΡΡΡ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΡΡ ΠΏΡΠΈΡΡΡΠΈΠ΅ Π½Π΅ΠΊΠΎΡΠΎΡΡΠΌ Π‘ΠΠ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ Π²ΠΈΠ·ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠ°Π·Π»ΠΈΡΠ½ΠΎΠ³ΠΎ ΡΠΎΠ΄Π° ΡΠ΅ΠΎΡΠ΅ΠΌ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΏΠΎΡΡΠ°ΠΏΠ½ΠΎΠ³ΠΎ Π²ΠΎΡΠΏΡΠΎΠΈΠ·Π²Π΅Π΄Π΅Π½ΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π·Π°Π΄Π°Ρ ΠΈ ΠΈΠ½ΡΡ
Π΄Π΅ΠΌΠΎΠ½ΡΡΡΠ°ΡΠΈΠΉ. ΠΡΠ΅ ΡΡΠΎ Π΄Π΅Π»Π°Π΅Ρ ΡΠ°ΠΊΠΈΠ΅ ΡΠΈΡΡΠ΅ΠΌΡ Π²Π΅ΡΡΠΌΠ° ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°ΡΠ΅Π»ΡΠ½ΡΠΌΠΈ Π΄Π»Ρ ΡΠΊΠΎΠ»ΡΠ½ΠΎΠ³ΠΎ ΠΈ Π΄Π°ΠΆΠ΅ Π²ΡΠ·ΠΎΠ²ΡΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ. Π‘Π»Π΅Π΄ΡΠ΅Ρ ΡΠ°ΠΊΠΆΠ΅ ΠΎΡΠΌΠ΅ΡΠΈΡΡ, ΡΡΠΎ Π‘ΠΠ ΠΏΡΠΈΠ·Π½Π°Π½Ρ Π²ΠΎ Π²ΡΠ΅ΠΌ ΠΌΠΈΡΠ΅ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΠΌ ΡΡΠ΅Π΄ΡΡΠ²ΠΎΠΌ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠ΅ Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎ-ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
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