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LECTURE GIVEN AT TH2002. Given a set of Boolean variables, and some constraints between them, is it possible to find a configuration of the variables which satisfies all constraints? This problem, which is at the heart of combinatorial…

Disordered Systems and Neural Networks · Physics 2009-11-07 Marc Mezard

The biomolecular networks underpinning cell function exhibit canalization, or the buffering of fluctuations required to function in a noisy environment. One understudied putative mechanism for canalization is the functional equivalence of a…

Molecular Networks · Quantitative Biology 2024-05-14 Austin M. Marcus , Jordan Rozum , Herbert Sizek , Luis M. Rocha

Polynomial dynamical systems are widely used to model and study real phenomena. In biochemistry, they are the preferred choice for modelling the concentration of chemical species in reaction networks with mass-action kinetics. These systems…

Algebraic Geometry · Mathematics 2014-12-30 Elisenda Feliu

The path probability of a particle undergoing stochastic motion is studied by the use of functional technique, and the general formula is derived for the path probability distribution functional. The probability of finding paths inside a…

Statistical Mechanics · Physics 2016-02-16 Masayuki Hattori , Sumiyoshi Abe

We study the biased random walk process in random uncorrelated networks with arbitrary degree distributions. In our model, the bias is defined by the preferential transition probability, which, in recent years, has been commonly used to…

Disordered Systems and Neural Networks · Physics 2013-05-29 Agata Fronczak , Piotr Fronczak

As a discrete approach to genetic regulatory networks, Boolean models provide an essential qualitative description of the structure of interactions among genes and proteins. Boolean models generally assume only two possible states…

Molecular Networks · Quantitative Biology 2007-05-23 Madalena Chaves , Eduardo D. Sontag , Reka Albert

Living systems operate in a critical dynamical regime -- between order and chaos -- where they are both resilient to perturbation, and flexible enough to evolve. To characterize such critical dynamics, the established 'structural theory' of…

Molecular Networks · Quantitative Biology 2022-01-28 Santosh Manicka , Manuel Marques-Pita , Luis M. Rocha

This paper describes and discusses Bayesian Neural Network (BNN). The paper showcases a few different applications of them for classification and regression problems. BNNs are comprised of a Probabilistic Model and a Neural Network. The…

Machine Learning · Computer Science 2018-01-31 Vikram Mullachery , Aniruddh Khera , Amir Husain

We investigate the dynamical behavior of simple modules composed of two genes with two or three regulating connections. Continuous dynamics for mRNA and protein concentrations is compared to a Boolean model for gene activity. Using a…

Molecular Networks · Quantitative Biology 2013-05-29 Eva Gehrmann , Barbara Drossel

Boolean networks have long been used as models of molecular networks and play an increasingly important role in systems biology. This paper describes a software package, Polynome, offered as a web service, that helps users construct Boolean…

Typical properties of computing circuits composed of noisy logical gates are studied using the statistical physics methodology. A growth model that gives rise to typical random Boolean functions is mapped onto a layered Ising spin system,…

Disordered Systems and Neural Networks · Physics 2015-05-18 Alexander Mozeika , David Saad , Jack Raymond

Boolean functions are mathematical objects used in diverse domains and have been actively researched for several decades already. One domain where Boolean functions play an important role is cryptography. There, the plethora of settings one…

Cryptography and Security · Computer Science 2023-01-20 Marko Djurasevic , Domagoj Jakobovic , Luca Mariot , Stjepan Picek

Polynomial functions have plenty of useful analytical properties, but they are rarely used as learning models because their function class is considered to be restricted. This work shows that when trained properly polynomial functions can…

Machine Learning · Computer Science 2021-06-30 Li-Ping Liu , Ruiyuan Gu , Xiaozhe Hu

Conventional and current wisdom assumes that the brain represents probability as a continuous number to many decimal places. This assumption seems implausible given finite and scarce resources in the brain. Quantization is an information…

Neurons and Cognition · Quantitative Biology 2020-01-07 James Tee , Desmond P. Taylor

Despite their apparent simplicity, random Boolean networks display a rich variety of dynamical behaviors. Much work has been focused on the properties and abundance of attractors. The topologies of random Boolean networks with one input per…

Disordered Systems and Neural Networks · Physics 2009-11-11 Björn Samuelsson , Carl Troein

It is shown that a large class of events in a product probability space are highly sensitive to noise, in the sense that with high probability, the configuration with an arbitrary small percent of random errors gives almost no prediction…

Probability · Mathematics 2008-11-26 Itai Benjamini , Gil Kalai , Oded Schramm

We consider communication over a noisy network under randomized linear network coding. Possible error mechanism include node- or link- failures, Byzantine behavior of nodes, or an over-estimate of the network min-cut. Building on the work…

Information Theory · Computer Science 2007-11-27 Andrea Montanari , Ruediger Urbanke

Complex systems often have features that can be modeled by advanced mathematical tools [1]. Of special interests are the features of complex systems that have a network structure as such systems are important for modeling technological and…

Classical Physics · Physics 2019-06-13 Nikolay K. Vitanov , Kaloyan N. Vitanov , Zlatinka I. Dimitrova

We study the volatility of the output of a Boolean function when the input bits undergo a natural dynamics. For $n = 1,2,\ldots$, let $f_n:\{0,1\}^{m_n} \ra \{0,1\}$ be a Boolean function and $X^{(n)}(t)=(X_1(t),\ldots,X_{m_n}(t))_{t \in…

Probability · Mathematics 2015-07-14 Johan Jonasson Jeffrey E. Steif

We consider chance constrained optimization where it is sought to optimize a function while complying with constraints, both of which are affected by uncertainties. The high computational cost of realistic simulations strongly limits the…

Optimization and Control · Mathematics 2022-04-18 Julien Pelamatti , Rodolphe Le Riche , Céline Helbert , Christophette Blanchet-Scalliet