Coarse-grained Concepts Research Articles

Application of dissipative particle dynamics to interfacial systems: Parameterization and scaling

Dissipative Particle Dynamics (DPD) is a stochastic particle model that is able to simulate larger systems over longer time scales than atomistic modeling approaches by including the concept of coarse-graining. Whether standard DPD can cover the whole mesoscale by changing the level of coarse-graining is still an open issue. A scaling scheme originally developed by Füchslin et al. (2009) was here applied to interfacial systems as one of the most successful uses of the classical DPD method. In particular, equilibrium properties such as the interfacial tension were analyzed at different levels of coarse-graining for planar oil–water interfaces with and without surfactant. A scaling factor for the interfacial tension was found due to the combined effect of the scaling scheme and the coarse-graining parameterization. Although the level of molecular description was largely decreased, promising results showed that it is possible to conserve the interfacial tension trend at increasing surfactant concentrations, remarkably reducing modeling complexity. The same approach was also employed to simulate a droplet configuration. Both planar and droplet conformations were maintained, showing that typical domain formations of multi-component systems can be performed in DPD by means of the scaling procedure. Therefore, we explored the possibility of describing oil–water and oil–water–surfactant systems in standard DPD using a scaling scheme with the aim of highlighting its advantages and limitations.

Visual Entity Linking via Multi-modal Learning

Abstract Existing visual scene understanding methods mainly focus on identifying coarse-grained concepts about the visual objects and their relationships, largely neglecting fine-grained scene understanding. In fact, many data-driven applications on the Web (e.g., news-reading and e-shopping) require accurate recognition of much less coarse concepts as entities and proper linking them to a knowledge graph (KG), which can take their performance to the next level. In light of this, in this paper, we identify a new research task: visual entity linking for fine-grained scene understanding. To accomplish the task, we first extract features of candidate entities from different modalities, i.e., visual features, textual features, and KG features. Then, we design a deep modal-attention neural network-based learning-to-rank method which aggregates all features and maps visual objects to the entities in KG. Extensive experimental results on the newly constructed dataset show that our proposed method is effective as it significantly improves the accuracy performance from 66.46% to 83.16% compared with baselines.

Multiresolution analysis of information flows from international carbon trading market to the clean energy stock market

This paper introduced a novel method to investigate the information flows from the international carbon trading market to the market for renewable energy stocks. The method combined the advantages of wavelet decomposition, transfer entropy, and complex network, which defined multiscale, directional, and dynamic flows of information. This analysis selected the daily futures prices of the EUA futures and the daily spot prices of the S&P Global Clean Energy Index (S&P GCE) as sample data and decomposed the bivariate time series into seven sequences for various time–frequency domains by maximal overlap discrete wavelet transform. Transfer entropy has been used to measure the process of transmission of information from the carbon trading market to the renewable energy stock market. The transfer entropies were transformed into four symbols and constructed directed and weighted complex networks of the transfer entropy fluctuation mode sequence in different time–frequency domains according to the concept of coarse graining. The findings showed that the carbon market information flows to the renewable energy stock market varied at different scales. In the short to medium term (2–16 days), there were a lot of information flows from EUA to S&P GCE. The information flow of EUA to S&P GCE is the most stable at scale d2 (4–8 days), but the volatility of the carbon market has not affected the clean energy market at scales d5–d6 (32–128 days). These findings provided the requisite reference for investors in carbon finance and policy-makers who support clean energy production.

What Is Bayesian Confirmation for?

ABSTRACTPeter Brössel and Franz Huber in 2015 argued that the Bayesian concept of confirmation had no use. I will argue that it has both the uses they discussed—it can be used for making claims about how worthy of belief various hypotheses are, and it can be used to measure the epistemic value of experiments. Furthermore, it can be useful in explanations. More generally, I will argue that more coarse-grained concepts (like confirmation) can be useful, even when we have more fine-grained concepts (like credences).

Mesoscopic Impurities Expose a Nucleation-Limited Regime of Crystal Growth.

Nanoscale self-assembly is naturally subject to impediments at the nanoscale. The recently developed ability to follow processes at the molecular level forces us to resolve older, coarse-grained concepts in terms of their molecular mechanisms. In this Letter, we highlight one such example. We present evidence based on experimental and simulation data that one of the cornerstones of crystal growth theory, the Cabrera-Vermilyea model of step advancement in the presence of impurities, is based on incomplete physics. We demonstrate that the piercing of an impurity fence by elementary steps is not solely determined by the Gibbs-Thomson effect, as assumed by Cabrera-Vermilyea. Our data show that for conditions leading up to growth cessation, step retardation is dominated by the formation of critically sized fluctuations. The growth recovery of steps is counter to what is typically assumed, not instantaneous. Our observations on mesoscopic impurities for lysozyme expose a nucleation-dominated regime of growth that has not been hitherto considered, where the system alternates between zero and near-pure velocity. The time spent by the system in arrest is the nucleation induction time required for the step to amass a supercritical fluctuation that pierces the impurity fence.

Experimental comparison of coarse-grained concepts in UML, OML, and TOS

Object-oriented approaches offer coarse-grained modeling concepts to structure complex application systems consisting of hundreds or even thousands of classes. This paper analyses such concepts by using a software engineering experiment. Two factors are controlled: Factor A consists of three object-oriented approaches (unified modeling language (UML), OPEN modeling language (OML), and taxonomic objectsystem (TOS)), and factor B consists of two application systems (a database-oriented application and a process-oriented application). The results show that the coarse-grained concepts of the object-oriented approaches OML and TOS are superior to those of UML when modeling a database-oriented application. From the results it can be concluded that the coarse-grained concepts of UML should be improved in further releases. The data were analysed by using advanced statistical methods.

Numerical Experiments on Trajectory Instabilities in Dissipative Systems

It is confirmed by computer experiments that a probabilistic approach is necessary for description of the dynamics of dissipative systems with unstable trajectories, although given equations of motion are deterministic. How initially close points in phase space do spread with time is examined. From this it is shown that the conditional probabilities can be defined naturally by introducing the concept of coarse graining in phase space and time, and moreover they satisfy a definition of a Markov process. The effect of external random forces is investigated by adding the Langevin forces to the equations of motion. It is suggested that the statistical properties of unstable dissipative systems are determined mainly by the inherent stochasticity due to the nonlinearity of the systems, unlike equilibrium systems whose statistical properties are governed by external random forces coming from contact with a heat reservoir.

Conditional expectations on von Neumann algebras: A new approach

In the framework of non-commutative probability theory on von Neumann algebras the concept of conditional expectation is redefined in such a way that it exists relative to arbitrary a priori distributions and subalgebras. We derive explicit expressions for the expectations relative to Abelian subalgebras and establish their connection with the concept of coarse-graining.