26 papers · ranked by Valyu relevance
Joshua Liddy, Michael Busa, Li Li, Brad Manor
The goal of this paper is to highlight considerations and provide recommendations for analytical issues that arise when applying entropy methods, specifically Sample Entropy (SampEn), to temporally correlated stochastic datasets, which are representative of a broad range of biomechanical and physiological variables. To…
Antoni Morro, Vincent Canals, Antoni Oliver, Miquel L. Alomar + 2 more
'Josep L. Rossello' 'Frederique Lisacek'] Minimal hardware implementations able to cope with the processing of large amounts of data in reasonable times are highly desired in our information-driven society. In this work we review the application of stochastic computing to probabilistic-based pattern-recognition…
Mark D. McDonnell, Derek Abbott, Karl J. Friston
Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations-e.g., random noise-cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter…
Theodoros Economou, Freya Garry
Understanding changes in extreme compound hazard events is important for climate mitigation and policy. By definition, such events are rare so robust quantification of their future changes is challenging. An approach is presented, for probabilistic modelling and simulation of climate model data, which is invariant to…
Babak M. S. Arani, Stephen R. Carpenter, Egbert H. van Nes, Ingrid A. van de Leemput + 3 more
Tipping points and alternative attractors have become an important focus of research and public discussions about the future of climate, ecosystems and societies. However, empirical evidence for the existence of alternative attractors remains scarce. For example, bimodal frequency distributions of state variables may…
Babak M. S. Arani, Egbert H. van Nes, Marten Scheffer
Potential analysis is used in many ecological studies to infer whether or not an ecosystem can have alternative stable states, to estimate the tipping points and, to assess the resilience of ecosystems. The main reason behind its frequent use is that such a frequency-based analysis is a minimalistic modelling strategy…
J. L. Callaham, J.-C. Loiseau, G. Rigas, S. L. Brunton
Many physical systems characterized by nonlinear multiscale interactions can be modelled by treating unresolved degrees of freedom as random fluctuations. However, even when the microscopic governing equations and qualitative macroscopic behaviour are known, it is often difficult to derive a stochastic model that is…
Christoph Zimmer, Ramon Grima
Computational modeling is widely used to deepen the understanding of biological processes. Due to advances in experimental techniques (e.g. the possibility to measure small numbers of molecules in single cells ), the importance of stochastic modeling is increasing. This article focuses on experimental time course data…
S. Kari
- The semiconductor and IC industry is facing with the issue of high energy consumption. In modern days computers and processing systems are designed based on Turing machine and Von Neumann's architecture. This architecture mainly focused on designing systems based on deterministic behaviors. To tackle energy…
Jared Callaham, Jean-Christophe Loiseau, Georgios Rigas, Steven L. Brunton
'Steven L. Brunton'] Many physical systems characterized by nonlinear multiscale interactions can be effectively modeled by treating unresolved degrees of freedom as random fluctuations. However, even when the microscopic governing equations and qualitative macroscopic behavior are known, it is often difficult to…
Philip Rinn, Pedro G. Lind, Matthias Wächter, Joachim Peinke
We describe an R package developed by the research group Turbulence, Wind energy and Stochastics (TWiSt) at the Carl von Ossietzky University of Oldenburg, which extracts the (stochastic) evolution equation underlying a set of data or measurements. The method can be directly applied to data sets with one or two…
Alex N Popinga, Jack Forman, Dmitri Svetlov, Huy Vo + 1 more
Biological data is prone to both intrinsic and extrinsic noise and variability between experimental replicas. That same stochasticity and heterogeneity can carry information about underlying biochemical mechanisms but, if not incorporated in modeling and probabilistic inference, can also bias parameter estimates and…
David H. Wolpert, Michael Holton Price, Stefani A. Crabtree, Timothy A. Kohler + 5 more
'Timothy A. Kohler' 'Jürgen Jost' 'James Evans' 'Peter F. Stadler' 'Hajime Shimao' 'Manfred D. Laubichler'] Historical processes manifest remarkable diversity. Nevertheless, scholars have long attempted to identify patterns and categorize historical actors and influences with some success. A stochastic process…
Éric Bertin
In this short note, we try to provide the reader with a brief pedagogical account of some similarities and differences between stochastic and deterministic processes. A short presentation of some basic notions related to the mathematical description of stochastic processes is also given. Our main aim is to illustrate…
Giuseppe Boccignone
The most simple kind of stochastic process is the Purely Random Process in which there are no correlations. From Eq. (25): (7-1)$P(1,2)\ =\ P(1)P(2)\tag{28}$ (7-2)$P(1,2,3)\ =\ P(1)P(2)P(3)$ (7-3)$P(1,2,3,4)\ =\ P(1)P(2)P(3)P_{1}(3)$ One such process can be obtained for example by repeated coin tossing. The complete…
Nerea Abrego, Sonja Saine, Reijo Penttilä, Brendan Furneaux + 7 more
'Tuija Hytönen' 'Otto Miettinen' 'Norman Monkhouse' 'Raisa Mäkipää' 'Jorma Pennanen' 'Evgeny V. Zakharov' 'Otso Ovaskainen'] Stochasticity is a main process in community assembly. However, experimental studies rarely target stochasticity in natural communities, and hence experimental validation of stochasticity…
Michael W. Klymkowsky, Katja Koehler, Melanie M. Cooper
A number of research studies indicate that students often have difficulties in understanding the presence and/or the implications of stochastic processes within biological systems. While critical to a wide range of phenomena, the presence and implications of stochastic processes are rarely explicitly considered in the…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Vincent Wagner, Nicole Radde
The Chemical Master Equation is a set of linear differential equations that describes the evolution of the probability distribution on all possible configurations of a (bio-)chemical reaction system. Since the number of configurations and therefore the dimension of the CME rapidly increases with the number of…
Pier Paolo Poir, Louis Lagardère, Jean-Philip Piquemal
We propose a new strategy to solve the Tkatchenko-Scheffler Many-Body Dispersion (MBD) model’s equations. Our approach overcomes the original O(N**3) computational complexity that limits its applicability to large molecular systems within thecontext of O(N) Density Functional Theory (DFT). First, in order to generate…
Teemu Kuosmanen, Alexandre Minetto, Ville Mustonen
Stochasticity plays an important role in all biological systems. The standard way to deal with stochasticity involves averaging over an ensemble of independent realizations. However, such mean statistics need not accurately reflect the typical outcomes in any finite sample unless the system satisfies the property of…
Authors not listed
Molecular Polariton is becoming one of the leading directions to control a multitude of chemical and physical processes, such as charge transfer, selective bond breaking, and excited state dynamics. Accurately and efficiently simulating polariton properties under the collective coupling regimes (between $N$ molecules…
Thomas Lynn, Julio Ottino, Richard Lueptow, Paul Umbanhowar
Cut-and-shuffle mixing is an instructive candidate system with which to assess the potential of machine learning (ML) as an approach to solve difficult mixing problems. We focus on a specific subset of cut-and-shuffle systems, the one-dimensional interval exchange transform. This class of mixing operations is well…
Authors not listed
Metastable states and the conformational transitions in between them are key to understanding dynamical behaviour and function of large-scale molecular systems. By combining basic dimensionality reduction techniques with a state-of-the art approximation of the Koopman operator associated to molecular dynamics…
David Lundberg, Landon Kilgallon, Julian Cooper, Francesca Starvaggi + 2 more
An understanding of monomer sequence is required to predict and engineer the properties of copolymers. Typically, comonomer sequence is inferred or determined from reactivity ratios, which are measured through copolymerization experiments. The accurate determination of reactivity ratios from copolymerizations where one…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…