Search results for "Entropy"
showing 10 items of 496 documents
Partial Stabilization of Input-Output Contact Systems on a Legendre Submanifold
2017
This technical note addresses the structure preserving stabilization by output feedback of conservative input-output contact systems, a class of input-output Hamiltonian systems defined on contact manifolds. In the first instance, achievable contact forms in closed-loop and the associated Legendre submanifolds are analysed. In the second instance the stability properties of a hyperbolic equilibrium point of a strict contact vector field are analysed and it is shown that the stable and unstable manifolds are Legendre submanifolds. In the third instance the consequences for the design of stable structure preserving output feedback are derived: in closed-loop one may achieve stability only rel…
Summarizing Large Scale 3D Mesh
2018
International audience; Recent progress in 3D sensor devices and in semantic mapping allows to build very rich HD 3D maps very useful for autonomous navigation and localization. However , these maps are particularly huge and require important memory capabilities as well computational resources. In this paper, we propose a new method for summarizing a 3D map (Mesh) as a set of compact spheres in order to facilitate its use by systems with limited resources (smartphones, robots, UAVs, ...). This vision-based summarizing process is applied in a fully automatic way using jointly photometric, geometric and semantic information of the studied environment. The main contribution of this research is…
Using Inverse Reinforcement Learning with Real Trajectories to Get More Trustworthy Pedestrian Simulations
2020
Reinforcement learning is one of the most promising machine learning techniques to get intelligent behaviors for embodied agents in simulations. The output of the classic Temporal Difference family of Reinforcement Learning algorithms adopts the form of a value function expressed as a numeric table or a function approximator. The learned behavior is then derived using a greedy policy with respect to this value function. Nevertheless, sometimes the learned policy does not meet expectations, and the task of authoring is difficult and unsafe because the modification of one value or parameter in the learned value function has unpredictable consequences in the space of the policies it represents…
Efficient Algorithms for Sequence Analysis with Entropic Profiles
2017
Entropy, being closely related to repetitiveness and compressibility, is a widely used information-related measure to assess the degree of predictability of a sequence. Entropic profiles are based on information theory principles, and can be used to study the under-/over-representation of subwords, by also providing information about the scale of conserved DNA regions. Here, we focus on the algorithmic aspects related to entropic profiles. In particular, we propose linear time algorithms for their computation that rely on suffix-based data structures, more specifically on the truncated suffix tree (TST) and on the enhanced suffix array (ESA). We performed an extensive experimental campaign …
Spectral entropy based neuronal network synchronization analysis based on microelectrode array measurements
2016
Synchrony and asynchrony are essential aspects of the functioning of interconnected neuronal cells and networks. New information on neuronal synchronization can be expected to aid in understanding these systems. Synchronization provides insight in the functional connectivity and the spatial distribution of the information processing in the networks. Synchronization is generally studied with time domain analysis of neuronal events, or using direct frequency spectrum analysis, e.g., in specific frequency bands. However, these methods have their pitfalls. Thus, we have previously proposed a method to analyze temporal changes in the complexity of the frequency of signals originating from differ…
Measuring spectrally-resolved information transfer.
2020
Information transfer, measured by transfer entropy, is a key component of distributed computation. It is therefore important to understand the pattern of information transfer in order to unravel the distributed computational algorithms of a system. Since in many natural systems distributed computation is thought to rely on rhythmic processes a frequency resolved measure of information transfer is highly desirable. Here, we present a novel algorithm, and its efficient implementation, to identify separately frequencies sending and receiving information in a network. Our approach relies on the invertible maximum overlap discrete wavelet transform (MODWT) for the creation of surrogate data in t…
A deeper look into natural sciences with physics-based and data-driven measures
2021
Summary With the development of machine learning in recent years, it is possible to glean much more information from an experimental data set to study matter. In this perspective, we discuss some state-of-the-art data-driven tools to analyze latent effects in data and explain their applicability in natural science, focusing on two recently introduced, physics-motivated computationally cheap tools—latent entropy and latent dimension. We exemplify their capabilities by applying them on several examples in the natural sciences and show that they reveal so far unobserved features such as, for example, a gradient in a magnetic measurement and a latent network of glymphatic channels from the mous…
Free energy profiles for two ubiquitous damaging agents: methylation and hydroxylation of guanine in B-DNA
2017
International audience; DNA methylation and hydroxylation are two ubiquitous reactions in DNA damage induction, yet insights are scarce concerning the free energy of activation within B-DNA. We resort to multiscale simulations to investigate the attack of a hydroxyl radical and of the primary diazonium onto a guanine embedded in a solvated dodecamer. Reaction free energy profiles characterize two strongly exergonic processes, yet allow unprecedented quantification of the barrier towards this damage reaction, not higher than 6 kcal mol−1 and sometimes inexistent, and of the exergonicities. In the case of the [G(C8)-OH]˙ intermediate, we challenge the functional dependence of such simulations…
Analyzing the feasibility of time correlated spectral entropy for the assessment of neuronal synchrony
2016
In this paper, we study neuronal network analysis based on microelectrode measurements. We search for potential relations between time correlated changes in spectral distributions and synchrony for neuronal network activity. Spectral distribution is quantified by spectral entropy as a measure of uniformity/complexity and this measure is calculated as a function of time for the recorded neuronal signals, i.e., time variant spectral entropy. Time variant correlations in the spectral distributions between different parts of a neuronal network, i.e., of concurrent measurements via different microelectrodes, are calculated to express the relation with a single scalar. We demonstrate these relati…
Calculation of the chemical potential and the activity coefficient of two layers of CO2 adsorbed on a graphite surface.
2014
We study the adsorption of carbon dioxide at a graphite surface using the new Small System Method, and find that for the temperature range between 300 K and 550 K most relevant for CO2 separation; adsorption takes place in two distinct thermodynamic layers defined according to Gibbs. We calculate the chemical potential and the activity coefficient of both layers directly from the simulations. Based on thermodynamic relations, the entropy and enthalpy of the CO2 adsorbed layers are also obtained. Their values indicate that there is a trade-off between entropy and enthalpy when a molecule chooses for one of the two layers. The first layer is a densely packed monolayer of relatively constant e…