Search results for "Probability distribution"
showing 10 items of 263 documents
Work fluctuations in bosonic Josephson junctions
2016
We calculate the first two moments and full probability distribution of the work performed on a system of bosonic particles in a two-mode Bose-Hubbard Hamiltonian when the self-interaction term is varied instantaneously or with a finite-time ramp. In the instantaneous case, we show how the irreversible work scales differently depending on whether the system is driven to the Josephson or Fock regime of the bosonic Josephson junction. In the finite-time case, we use optimal control techniques to substantially decrease the irreversible work to negligible values. Our analysis can be implemented in present-day experiments with ultracold atoms and we show how to relate the work statistics to that…
An autonomous petrological database for geodynamic simulations of magmatic systems
2022
SUMMARY Self-consistent modelling of magmatic systems is challenging as the melt continuously changes its chemical composition upon crystallization, which may affect the mechanical behaviour of the system. Melt extraction and subsequent crystallization create new rocks while depleting the source region. As the chemistry of the source rocks changes locally due to melt extraction, new calculations of the stable phase assemblages are required to track the rock evolution and the accompanied change in density. As a consequence, a large number of isochemical sections of stable phase assemblages are required to study the evolution of magmatic systems in detail. As the state-of-the-art melting diag…
SHAPE MATTERS: EFFECT OF POINT MUTATIONS ON RNA SECONDARY STRUCTURE
2013
A suitable model to dive into the properties of genotype-phenotype landscapes is the relationship between RNA sequences and their corresponding minimum free energy secondary structures. Relevant issues related to molecular evolvability and robustness to mutations have been studied in this framework. Here, we analyze the one-mutant neighborhood of the predicted secondary structure of 46 different RNAs, including tRNAs, viroids, larger molecules such as Hepatitis-δ virus, and several random sequences. The probability distribution of the effect of point mutations in linear structural motifs of the secondary structure is well fit by Pareto or Lognormal probability distributions functions, indep…
Novel Threshold Calculations for Remaining Useful Lifetime Estimation of Rolling Element Bearings
2018
The prognostics objective is to avoid sudden machinery breakdowns and to estimate the remaining useful life after initial degradation. Typically, physical health indicators are derived from available sensor data, and a mathematical model is tuned to fit them. The time it takes for the model to reach a failure threshold is the estimated remaining useful life. The failure threshold may be determined from historical failure data, but that is not always readily available. ISO standard 10816–3 defines permissible velocity vibration levels for machines that may be used as a failure threshold. However, velocity vibration is not suitable for bearing prognostics due to the effect of integration from…
The probabilistic pickup-and-delivery travelling salesman problem
2019
Abstract Transportation problems are essential in commercial logistics and have been widely studied in the literature during the last decades. Many of them consist in designing routes for vehicles to move commodities between locations. This article approaches a pickup-and-delivery single-vehicle routing problem where there is susceptibility to uncertainty in customer requests. The probability distributions of the requests are assumed to be known, and the objective is to design an a priori route with minimum expected length. The problem has already been approached in the literature, but through a heuristic method. This article proposes the first exact approach to the problem. Two mathematica…
VARIABLE SELECTION FOR NOISY DATA APPLIED IN PROTEOMICS
2014
International audience; The paper proposes a variable selection method for pro-teomics. It aims at selecting, among a set of proteins, those (named biomarkers) which enable to discriminate between two groups of individuals (healthy and pathological). To this end, data is available for a cohort of individuals: the biological state and a measurement of concentrations for a list of proteins. The proposed approach is based on a Bayesian hierarchical model for the dependencies between biological and instrumental variables. The optimal selection function minimizes the Bayesian risk, that is to say the selected set of variables maximizes the posterior probability. The two main contributions are: (…
Conditional Versus Joint Probability Assessments
1984
AbstractThe assessment of conditional and / or joint probabilities of events that constitute scenarios is necessary for sound planning, forecasting, and decision making. The assessment process is complex and subtle, and various difficulties are encountered in the elicitation of such probabilities such as, implicit violations ofthe probability calculus and some meaningfjilness conditions. The necessary and sufficient as well as meaningfulness conditions that the elicited information on conditional and joint probabilities must satisfy are evaluated against actual assessments empirically. A high frequency of violation of these conditions was observed in assessing both conditional and joint pro…
Partitioned learning of deep Boltzmann machines for SNP data.
2016
Abstract Motivation Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals. Results After a brief overview of how deep Boltzmann machines (DBMs), a deep learning approach, can be adapted to SNP data in principle, we specifically present a way to alleviate the dimensionality problem by partitioned learning. We propose a sparse regression approach to coarsely screen…
Delay-Probability-Distribution-Dependent FIR Filtering Design with Envelope Constraints
2013
Published version of an article in the journal: Mathematical Problems in Engineering. Also available from the publisher at: http://dx.doi.org/10.1155/2013/930927 Open Access This paper studies the problem of H∞ finite-impulse response (FIR) filtering design of time-delay system. The time-delay considered here is time-varying meanwhile with a certain stochastic characteristic, and the probability of delay distribution is assumed to be known. Furthermore, the requirement of pulse-shape is also considered in filter design. Employing the information about the size and probability distribution of delay, a delay-probability-distribution-dependent criterion is proposed for the filtering error syst…
Using machine learning to disentangle LHC signatures of Dark Matter candidates
2019
We study the prospects of characterising Dark Matter at colliders using Machine Learning (ML) techniques. We focus on the monojet and missing transverse energy (MET) channel and propose a set of benchmark models for the study: a typical WIMP Dark Matter candidate in the form of a SUSY neutralino, a pseudo-Goldstone impostor in the shape of an Axion-Like Particle, and a light Dark Matter impostor whose interactions are mediated by a heavy particle. All these benchmarks are tensioned against each other, and against the main SM background ($Z$+jets). Our analysis uses both the leading-order kinematic features as well as the information of an additional hard jet. We explore different representa…