Data Structures and Algorithms

1811 Submissions

[4] viXra:1811.0447 [pdf] submitted on 2018-11-27 16:14:33

Thinking and Rethinking on the Cyberscale

Authors: Savior F. Eason
Comments: 23 Pages. Technology actively in process of research. Not all claims have been verified, or currently foreshadow future advancements yet to be confirmed.

Motherboxes, PM motherboards, and a new data processor. Also details on a pattern I discovered in the laws of physics allowing the algorithm of logopolitan conversion, which would allow a computer of sufficient power to convert matter into structure, and the structure into math, allowing us to create truly simulated universes, and use them as computational power sources to reverse that process, allowing fascinating advancements in quantum physics. Logopolitan computers could also be integrated in Cyber-Neural fusion to advance our brains, allowing them to interpret quantum code and convert it into computer code, and that code into Neural data, creating humans of immeasurable IQ and mental capabilities. While this area isn't quite the place for positing the philosophies of what this would mean, one could imagine what we could do if we simply accepted this tech and put in the resources to achieve it.
Category: Data Structures and Algorithms

[3] viXra:1811.0358 [pdf] submitted on 2018-11-22 09:31:27

Surprising Power of Small Data

Authors: George Rajna
Comments: 53 Pages.

The power of the method Bayati and his colleagues outline is that it can be used to pursue multiple goals at once. [37] Using micromagnetic simulation, scientists have found the magnetic parameters and operating modes for the experimental implementation of a fast racetrack memory module that runs on spin current, carrying information via skyrmionium, which can store more data and read it out faster. [36] Scientists at the RDECOM Research Laboratory, the Army's corporate research laboratory (ARL) have found a novel way to safeguard quantum information during transmission, opening the door for more secure and reliable communication for warfighters on the battlefield. [35] Encrypted quantum keys have been sent across a record-breaking 421 km of optical fibre at the fastest data rate ever achieved for long-distance transmission. [34] The companies constructed an application for data transmission via optical fiber lines, which when combined with high-speed quantum cryptography communications technologies demonstrated practical key distribution speeds even in a real-world environment. [33] Nanosized magnetic particles called skyrmions are considered highly promising candidates for new data storage and information technologies. [32] They do this by using "excitons," electrically neutral quasiparticles that exist in insulators, semiconductors and in some liquids. [31] Researchers at ETH Zurich have now developed a method that makes it possible to couple such a spin qubit strongly to microwave photons. [30] Quantum dots that emit entangled photon pairs on demand could be used in quantum communication networks. [29] Researchers successfully integrated the systems—donor atoms and quantum dots. [28] A team of researchers including U of A engineering and physics faculty has developed a new method of detecting single photons, or light particles, using quantum dots. [27] Recent research from Kumamoto University in Japan has revealed that polyoxometalates (POMs), typically used for catalysis, electrochemistry, and photochemistry, may also be used in a technique for analyzing quantum dot (QD) photoluminescence (PL) emission mechanisms. [26]
Category: Data Structures and Algorithms

[2] viXra:1811.0302 [pdf] submitted on 2018-11-19 10:58:07

A New Approach in Content-Based Image Retrieval Neutrosophic Domain

Authors: A. A. Salama, Mohamed Eisa, Hewayda ElGhawalby, A. E. Fawzy
Comments: 10 Pages.

Theaimofthischapteristopresenttexturefeaturesforimagesembedded in the neutrosophic domain with Hesitancy degree. Hesitancy degree is the fourth component of neutrosophic sets. The goal is to extract a set of features to represent the content of each image in the training database to be used for the purpose of retrieving images from the database similar to the image under consideration.
Category: Data Structures and Algorithms

[1] viXra:1811.0062 [pdf] replaced on 2018-11-05 06:35:15

P ≠ co-NP

Authors: Robert DiGregorio
Comments: 2 Pages. fix typos and clarify

We prove that class P ≠ class co-NP.
Category: Data Structures and Algorithms