A Primal–Dual Smooth Perceptron–von Neumann Algorithm

2013 ◽  
pp. 303-320 ◽  
Author(s):  
Negar Soheili ◽  
Javier Peña
Keyword(s):  
Von Neumann ◽  
Primal Dual ◽  
Von Neumann Algorithm

scholarly journals DNA synthesis for true random number generation

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Linda C. Meiser ◽  
Julian Koch ◽  
Philipp L. Antkowiak ◽  
Wendelin J. Stark ◽  
Reinhard Heckel ◽  
...  
Keyword(s):  
Random Number ◽  
Random Number Generation ◽  
Sensitive Information ◽  
Encrypted Data ◽  
Von Neumann ◽  
Number Generation ◽  
Sequencing Technologies ◽  
Encryption And Decryption ◽  
Dna Strands ◽  
Von Neumann Algorithm

AbstractThe volume of securely encrypted data transmission required by today’s network complexity of people, transactions and interactions increases continuously. To guarantee security of encryption and decryption schemes for exchanging sensitive information, large volumes of true random numbers are required. Here we present a method to exploit the stochastic nature of chemistry by synthesizing DNA strands composed of random nucleotides. We compare three commercial random DNA syntheses giving a measure for robustness and synthesis distribution of nucleotides and show that using DNA for random number generation, we can obtain 7 million GB of randomness from one synthesis run, which can be read out using state-of-the-art sequencing technologies at rates of ca. 300 kB/s. Using the von Neumann algorithm for data compression, we remove bias introduced from human or technological sources and assess randomness using NIST’s statistical test suite.

Lectures on von Neumann Algebras

2019 ◽  
Author(s):  
Serban-Valentin Stratila ◽  
Laszlo Zsido
Keyword(s):  
Von Neumann Algebras ◽  
Von Neumann

John von Neumann

2004 ◽  
Vol 174 (12) ◽  
pp. 1371 ◽  
Author(s):  
Mikhail I. Monastyrskii
Keyword(s):  
John Von Neumann ◽  
Von Neumann

Routing Optimization with Efficient Second Order Distributed Approach Using Congestion Control Rules

2017 ◽  
Vol 7 (7) ◽  
pp. 363
Author(s):  
Jaya Pratha Sebastiyar ◽  
Martin Sahayaraj Joseph
Keyword(s):  
Congestion Control ◽  
Second Order ◽  
Back Pressure ◽  
Order Method ◽  
Delay Performance ◽  
Routing Optimization ◽  
Distributed Approach ◽  
Control Rules ◽  
Primal Dual ◽  
Queue Stability

Distributed joint congestion control and routing optimization has received a significant amount of attention recently. To date, however, most of the existing schemes follow a key idea called the back-pressure algorithm. Despite having many salient features, the first-order sub gradient nature of the back-pressure based schemes results in slow convergence and poor delay performance. To overcome these limitations, the present study was made as first attempt at developing a second-order joint congestion control and routing optimization framework that offers utility-optimality, queue-stability, fast convergence, and low delay.  Contributions in this project are three-fold. The present study propose a new second-order joint congestion control and routing framework based on a primal-dual interior-point approach and established utility-optimality and queue-stability of the proposed second-order method. The results of present study showed that how to implement the proposed second-order method in a distributed fashion.

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