Reconstruction and Trial Verification of the Collatz Conjecture Based On Big Data

2021 ◽  
Author(s):  
Hongyuan Ye ◽  
Lei Zhang
Keyword(s):  
Big Data ◽  
Collatz Conjecture

scholarly journals The Proof of Collatz Conjecture

2020 ◽  
Author(s):  
Hongyuan Ye
Keyword(s):  
Big Data ◽  
Negative Integer ◽  
Leaf Node ◽  
Sufficient Condition ◽  
Computer Data ◽  
Collatz Conjecture ◽  
Large Numbers ◽  
Structure Tree ◽  
Mathematical Problems ◽  
Minimum Number

Abstract This paper redefines Collatz conjecture, and proposes strong Collatz conjecture, the strong Collatz conjecture is a sufficient condition for the Collatz conjecture. Based on the computer data structure–tree, we construct the non-negative integer inheritance decimal tree. The nodes on the decimal tree correspond to non-negative integers. We further define the Collatz-leaf node (corresponding to the Collatz-leaf integer) on the decimal tree. The Collatz-leaf nodes satisfy strong Collatz conjecture. Derivation through mathematics, we prove that the Collatz-leaf node (Collatz-leaf integer) has the characteristics of inheritance. With computer large numbers and big data calculation, we conclude that all nodes at depth 800 are Collatz-leaf nodes. So we prove that strong Collatz conjecture is true, the Collatz conjecture must also be true. And for any positive integer N greater than 1, the minimum number of Collatz transform times from N to 1 is log2 N, the maximum number of Collatz transform times is 800 *（N-1）. The non-negative integer inheritance decimal tree proposed and constructed in this paper also can be used for the proof of other mathematical problems.

Find Out About 'Big Data' to Track Outcomes

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  
Big Data

Find Out About 'Big Data' to Track Outcomes

U.S., Canada Collaborate on Big Data in ASD Research

ASHA Leader ◽  
2015 ◽  
Vol 20 (12) ◽  
pp. 16-16
Keyword(s):  
Big Data

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

“Big Data in Psychology: Methods and Applications”

2017 ◽  
Vol 225 (3) ◽  
pp. 287-288
Keyword(s):  
Big Data

An associated conference will take place at ZPID – Leibniz Institute for Psychology Information in Trier, Germany, on June 7–9, 2018. For further details, see: http://bigdata2018.leibniz-psychology.org

When the Sample Is the Population: Big Data

PsycCRITIQUES ◽  
2014 ◽  
Vol 59 (2) ◽  
Author(s):  
David J. Pittenger
Keyword(s):  
Big Data

Old Answers to New Questions: Turing Tests in the Era of Big DataOld Answers to New Questions: Turing Tests in the Era of Big Data

PsycCRITIQUES ◽  
2016 ◽  
Vol 6161 (1515) ◽  
Author(s):  
Michael Roess
Keyword(s):  
Big Data

A critical incident for big data

2014 ◽  
Author(s):  
Daniel Maurath
Keyword(s):  
Big Data ◽  
Critical Incident

Little sensors, big data

2015 ◽  
Author(s):  
Kirsten Weir
Keyword(s):  
Big Data
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