Results 71 to 80 of about 503,210 (300)
Neighborhood conditions and edge-disjoint perfect matchings
Discrete Mathematics, 1991 A graph \(G\) satisfies the all pairs neighborhood condition \(\text{ANC}(G)\geq m\) if, for each pair \(x,y\) of vertices of \(G\), we have \(| N_ G(x)\cup N_ G(y)|\geq m\). Let \(k\) be a fixed positive integer and \(G\) a graph of even order \(n\) which satisfies the following conditions: (1) the minimum degree of \(G\) is at least \(k+1\); (2) the ...
Faudree, R.J. +2 more
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Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing
Advanced Intelligent Systems, EarlyView.This Roadmap outlines the transformative role of artificial intelligence‐augmented additive manufacturing, highlighting advances in design, monitoring, and product development. By integrating tools such as generative design, computer vision, digital twins, and closed‐loop control, it presents pathways toward smart, scalable, and autonomous additive ...
Ali Zolfagharian +37 more
wiley +1 more source
Experimenting with (Conditional) Perfection [PDF]
Conditional perfection is the phenomenon in which conditionals are strengthened to biconditionals. In some contexts, “If A, B” is understood as if it meant “A if and only if B.” We present and discuss a series of experiments designed to test one of the ...
Cariani, Fabrizio, Rips, Lance J.
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Software fault-freeness and reliability predictions [PDF]
, 2013 Many software development practices aim at ensuring that software is correct, or fault-free. In safety critical applications, requirements are in terms of probabilities of certain behaviours, e.g. as associated to the Safety Integrity Levels of IEC 61508.
A. Bertolino +10 more
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Perfect Probability Measures and Regular Conditional Probabilities
The Annals of Mathematical Statistics, 1966 In an attempt to refine the axiomatic model of a probability space introduced by Kolmogorov [11], Gnendenko and Kolmogorov [5] introduced the concept of a perfect probability measure. The desirability of some sort of refinement has been pointed out by several well known examples [2], [3], [8], which display a certain amount of pathology inherent in ...
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Advanced Intelligent Systems, EarlyView.The polymerase chain reaction (PCR).Perturbation Theory and Machine Learning framework integrates perturbation theory and machine learning to classify genetic sequences, distinguishing ancient DNA from modern controls and predicting tree health from soil metagenomic data.
Jose L. Rodriguez +19 more
wiley +1 more source
Advanced Intelligent Systems, EarlyView.Variational Autoencoder+Deep Deterministic Policy Gradient addresses low‐light failures of infrared depth sensing for indoor robot navigation. Stage 1 pretrains an attention‐enhanced Variational Autoencoder (Convolutional Block Attention Module+Feature Pyramid Network) to map dark depth frames to a well‐lit reconstruction, yielding a 128‐D latent code ...
Uiseok Lee +7 more
wiley +1 more source
Why is Deep Random suitable for cryptology
, 2017 We present a new form of randomness, called Deep Randomness, generated in such a way that probability distribution of the output signal is made unknowledgeable for an observer. By limiting, thanks to Deep Randomness, the capacity of the opponent observer
de Valroger, Thibault
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Conditions for $\beta$-perfectness
, 2000 A $\beta$-perfect graph is a simple graph $G$ such that $\chi(G')=\beta(G')$ for every induced subgraph $G'$ of $G$, where $\chi(G')$ is the chromatic number of $G'$, and $\beta(G')$ is defined as the maximum over all induced subgraphs $H$ of $G'$ of the minimum vertex degree in $H$.
Keijsper, J.C.M., Tewes, M.
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An information theoretic necessary condition for perfect reconstruction
, 2023 27 pages, 9 ...
Delsol, Idris +4 more
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