Random Configuration Research Articles

Effects of size polydispersity on random close-packed configurations of spherical particles.

We analyze the packing properties of simulated three-dimensional polydisperse samples of spherical particles assembled by mechanical compaction with zero interparticle friction, leading to random close-packed configurations of the highest packing fraction. The particle size distributions are generated from the incomplete beta distribution with three parameters: A size span and two shape parameters that control the curvature of the distribution function. For each size distribution, the number of particles is determined by accounting for the statistical representativity of all particle size classes in terms of both the numbers and volumes of particles. Remarkably, the packing fraction increases, up to a small variability, with an effective size span, known as the coefficient of uniformity, that combines the three control parameters of the distribution. The local particle environments are characterized by the particle connectivities and anisotropies, which unveil the class of particles with four contact neighbors as the largest class with an increasing population as a function of size span, indicating the higher stability of particles trapped by four larger particles. As a result of increasing topological inhomogeneity of the packings, the force distributions get increasingly broader with increasing effective size span. Finally, we find that larger particles do not always carry stronger average stresses, in particular when the particle size distribution allows for a sufficiently large number of small particles.

Opinion forming in Erdős–Rényi random graph and expanders

Assume for a graph G=V,E and an initial configuration, where each node is blue or red, in each discrete-time round all nodes simultaneously update their color to the most frequent color in their neighborhood and a node keeps its color in case of a tie. We study the behavior of this basic process, which is called the majority model, on the Erdős–Rényi random graph Gn,p and regular expanders. First we consider the behavior of the majority model on Gn,p with an initial random configuration, where each node is blue independently with probability pb and red otherwise. It is shown that in this setting the process goes through a phase transition at the connectivity threshold, namely logn∕n. Furthermore, we say a graph G is λ-expander if the second-largest absolute eigenvalue of its adjacency matrix is λ. We prove that for a Δ-regular λ-expander graph if λ∕Δ is sufficiently small, then the majority model by starting from 1∕2−δn blue nodes (for an arbitrarily small constant δ>0) results in fully red configuration in sub-logarithmically many rounds. Roughly speaking, this means the majority model is an “efficient” and “fast” density classifier on regular expanders. As a by-product of our results, we show that regular Ramanujan graphs are asymptotically optimally immune, that is for an n-node Δ-regular Ramanujan graph if the initial number of blue nodes is s≤βn, the number of blue nodes in the next round is at most cs∕Δ for some constants c,β>0.

A Unified Video Codec for SDR, HDR, and 360° Video Applications

The ITU-T Video Coding Experts Group (VCEG) and ISO/IEC Moving Picture Experts Group (MPEG) issued in October 2017 a joint Call for Proposals (CfP) on video compression with capability beyond HEVC. The joint CfP included three categories of content: standard dynamic range (SDR), high dynamic range and wide color gamut (HDR/WCG), and 360° omni-directional video (360°). This paper describes a response to the joint CfP that considers all three categories of video content. The core codec in the response is designed based on the joint exploration model (JEM) reference software. The key coding tools in the JEM are significantly simplified to reduce both average and worst-case complexity for hardware design with negligible coding performance loss. Furthermore, two additional coding tools are used to further improve coding efficiency. For the HDR and 360° categories, additional coding tools specifically designed to optimize the compression efficiency and subjective quality of that specific content category are included. Further, some SDR coding tools are modified to alleviate subjective quality problems. For the random access configuration, compared to the HEVC test model (HM) anchor, the proposed video codec achieves average luma rate savings of 35.7%, 31.3%, and 33.9% for the SDR, HDR, and 360° categories, respectively.

On the geometric structures in evolutionary games on square and triangular lattices

We study a model of a spatial evolutionary game, based on the Prisoner’s dilemma for two regular arrangements of players, on a square lattice and on a triangular lattice. We analyze steady state distributions of players which evolve from irregular, random initial configurations. We find significant differences between the square and triangular lattice, and we characterize the geometric structures which emerge on the triangular lattice.

Unfolding and partial refolding of a cellulase from the SDS-denatured state: From β-sheet to α-helix and back

Globular proteins are typically unfolded by SDS to form protein-decorated micelle-like structures. Several proteins have been shown subsequently to refold by addition of the nonionic surfactant octaethylene glycol monododecyl ether (C12E8). Thus SDS converts β-lactoglobulin, which has mainly β-sheet secondary structure, into a state rich in α-helicality, while addition of C12E8 leads to refolding and recovery of the original β-sheet structure. Here we extend these studies to the large β-sheet-rich cellulase Cel7b from Humicola insolens whose enzymatic activity provides a very sensitive refolding parameter. The enzymes widespread usage in the detergent industry makes it an obvious model system for protein-surfactant interactions. SDS-unfolding and subsequent refolding using C12E8 were investigated at pH 4.2 using near- and far-UV circular dichroism (CD), small-angle X-ray scattering (SAXS), isothermal titration calorimetry (ITC), size-exclusion chromatography (SEC) and activity measurements. The Cel7b:SDS complex can be described as a random configuration of 3–4 connected core-shell structures in which the protein is converted to a mainly α-helical secondary structure. Addition of C12E8 recovers almost all the secondary structure, part of the tertiary structure, about 50% of the activity and dissociates part of the protein population completely from detergent micelles. The lack of complete refolding may be due to charge neutralisation of Cel7b by SDS, kinetically trapping the enzyme into aggregated structures. In support of this, aggregates did not form when C12E8 was first mixed with Cel7b followed by addition of SDS. Formation of such aggregates may be a general phenomenon hampering quantitative refolding from the SDS-denatured state.

Modeling active fault systems and seismic events by using a fiber bundle model – example case: the Northridge aftershock sequence

Abstract. Earthquake aftershocks display spatiotemporal correlations arising from their self-organized critical behavior. Dynamic deterministic modeling of aftershock series is challenging to carry out due to both the physical complexity and uncertainties related to the different parameters which govern the system. Nevertheless, numerical simulations with the help of stochastic models such as the fiber bundle model (FBM) allow the use of an analog of the physical model that produces a statistical behavior with many similarities to real series. FBMs are simple discrete element models that can be characterized by using few parameters. In this work, the aim is to present a new model based on FBM that includes geometrical characteristics of fault systems. In our model, the faults are not described with typical geometric measures such as dip, strike, and slip, but they are incorporated as weak regions in the model domain that could increase the likelihood to generate earthquakes. In order to analyze the sensitivity of the model to input parameters, a parametric study is carried out. Our analysis focuses on aftershock statistics in space, time, and magnitude domains. Moreover, we analyzed the synthetic aftershock sequences properties assuming initial load configurations and suitable conditions to propagate the rupture. As an example case, we have modeled a set of real active faults related to the Northridge, California, earthquake sequence. We compare the simulation results to statistical characteristics from the Northridge sequence determining which range of parameters in our FBM version reproduces the main features observed in real aftershock series. From the results obtained, we observe that two parameters related to the initial load configuration are determinant in obtaining realistic seismicity characteristics: (1) parameter P, which represents the initial probability order, and (2) parameter π, which is the percentage of load distributed to the neighboring cells. The results show that in order to reproduce statistical characteristics of the real sequence, larger πfrac values (0.85<πfrac<0.95) and very low values of P (0.0<P≤0.08) are needed. This implies the important corollary that a very small departure from an initial random load configuration (computed by P), and also a large difference between the load transfer from on-fault segments than by off-faults (computed by πfrac), is required to initiate a rupture sequence which conforms to observed statistical properties such as the Gutenberg–Richter law, Omori law, and fractal dimension.

The effect of heterogeneities in hydrate saturation on gas production from natural systems

Understanding the rate and time evolution of gas release from natural gas hydrate systems is important when evaluating the potential of gas hydrate as a future energy source, or the impact of gas from hydrate on climate. The release of gas from hydrate is heavily influenced by a number of factors, many of which vary through the hydrate system. The fundamental heterogeneity of natural gas hydrate systems is often poorly represented in models. Here we simulate depressurisation-induced gas production from a single vertical well in 34 models with heterogeneous 2D distributions of hydrate that include layered, columnar or random configurations and comparable models with homogenous saturation distributions. We found that the temporal evolution of gas production rate follows a consistent trend for all models, but at any time the gas production rate across the models varied by up to ±35% in the first year of production, and by up to ±25% thereafter. The primary control on the gas production rate is the overall amount of hydrate in the system, but local variations in hydrate saturation cause significant fluctuations in the time evolution of production. These hydrate variations can cause changes in the gas flow path through the system and associated drops in gas production rate continuing for multiple years. Overall, our results suggest that small levels of heterogeneity in hydrate systems can cause variations in the gas production rate similar in scale to much larger variations in homogenous systems. Our work provides an error margin for previously modelled gas production rates, and a note of caution for potential commercial development of gas hydrate.

An Accurate Prediction of Daylight Pipe Harvesting of Interior Space

The performance of a light guide under arbitrary meteorological conditions is difficult to predict numerically because none of the present methods can account for the random configuration of clouds. Although, partly cloudy skies typically occur very frequently in many regions over the globe, the effects of cloud sizes, distributions, or altitudes on the luminous effectiveness of a light guide remain virtually unknown. The physical installation of light guides does not allow for a controlled experiment in which a single parameter can be varied within a defined range, while holding all other parameters constant. Numerical modeling is the only practical way to provide deep insights on the role of a specific property, such as cloud coverage, and its importance relative to all others (e.g., atmospheric turbidity). We have developed a unique solution through linking the UniSky simulator and HOLIGILM (hollow light guide interior illumination method) tool, while providing for the first time a powerful method that can provide accurate predictions and understanding of the fundamental differences of light guide behaviors under homogeneous and inhomogeneous sky states. The use of homogeneous skies in low-accuracy empirical models often results in overestimated/underestimated effectiveness of some light-guide systems. The model we have developed can predict complex optical signatures that are normally impossible to reproduce using up-to date empirical models (e.g., the illuminance patterns due to isolated clouds or clouds traversing across the sky). The optical efficiency, average cosine and working-place illuminance are analyzed and compared in order to demonstrate the model capabilities.

Tortuosity and Percolation Probability on 3 Dimensional Rock Models with Different Model Sizes

Tortuosity, percolation probability and porosity are important rock parameters because all those things affect the fluid flow in rocks. In this study, 3-dimensional cube-shaped rock models were randomly generated with N × N × N size containing matrices and pores. Rock models were made with porosities ranging from 0.1 to 1 with increasing of value 0.1. The selected N is N = 5, 6, 7, 8, 9, 10, 25, 125 and for every N there are 10,000 random configurations. Then, tortuosity was calculated using a nearest-neighbor sites method with 6 neighboring sides. In addition, with the site percolation model, percolation probability was calculated by determining the comparison between the number of percolate configurations and the total number of configurations created. The results show that in large-size rock models, the average tortuosity will increase. Further, the percolation probability is zero at a small porosity and rises to 1 as the porosity increases. Moreover, the slope of the percolation probability curve will increase for the larger model. In other words, the larger the size of the model, the percolation threshold will become larger or more difficult to percolate for small porosity.

Deep Frame Prediction for Video Coding

We propose a novel frame prediction method using a deep neural network (DNN), with the goal of improving the video coding efficiency. The proposed DNN makes use of decoded frames, at both the encoder and decoder to predict the textures of the current coding block. Unlike conventional inter-prediction, the proposed method does not require any motion information to be transferred between the encoder and the decoder. Still, both the uni-directional and bi-directional predictions are possible using the proposed DNN, which is enabled by the use of the temporal index channel, in addition to the color channels. In this paper, we developed a jointly trained DNN for both uni-directional and bi-directional predictions, as well as separate networks for uni-directional and bi-directional predictions, and compared the efficacy of both the approaches. The proposed DNNs were compared with the conventional motion-compensated prediction in the latest video coding standard, High Efficiency Video Coding (HEVC), in terms of the BD-bitrate. The experiments show that the proposed joint DNN (for both uni-directional and bi-directional predictions) reduces the luminance bitrate by about 4.4%, 2.4%, and 2.3% in the low delay $P$ , low delay, and random access configurations, respectively. In addition, using the separately trained DNNs brings further bit savings of about 0.3%–0.5%.

Molecular dynamics study in the Ce0.9M0.1O1.95 (M=Gd, Sm) doped and co-doped CeO2 systems: Structure and oxygen diffusion

Doped ceria is known for its high ionic conductivity which is limited by the creation of defect clusters. We investigate using Molecular Dynamics the evolution of the structure and oxygen diffusivity of the Ce0.9M0.1O1.95 (M = Gd, Sm) doped and co-doped CeO2 systems. Two configurations concerning the introduction of the doping ions and oxygen vacancies inside the structure are generated and compared, the random one (R) and the neighbor one (N). The pair potential parameter set is chosen among three sets by comparison with experimental data. The pair correlation function first peak is fitted by a lognormal function to take into account the asymmetry. The evolution of the coefficient of thermal expansion, the coordination number and the bond lengths of the different ion pairs with the temperature, is presented. The influence of the local C-type sesquioxide M2O3 structure on those parameters is discussed. No significant positive co-doping effect on the oxygen diffusion process is revealed. The random configuration of the defects leads to the highest oxygen diffusivity and to the lower activation energy whatever the doping ion (Gd, Sm or Gd/Sm).

A Random Forests-based sensitivity analysis framework for assisted history matching

Sensitivity analysis is typically required to screen unwanted history matching parameters so that computational cost can be reduced. Random Forests is a well-known statistical learning tool that maps a list of input parameters onto a predicted response. A constructed Random Forests model ranks these input parameters efficiently even for highly nonlinear data. The aim of this study is to investigate the capability of Random Forests in assisted/automatic production history matching in reservoir engineering. A history matching case study has been chosen to rigorously compare the capability of Random Forests and one-parameter-at-time (OPAT) in sensitivity analysis. The capability of Random Forests as a sensitivity analysis tool was investigated and compared with OPAT. By implementing the optimal number of decision trees, which the Random Forests' configuration parameters, and the appropriate design of experiments method Fractional Factorial Design (FFD), the Random Forests-based history matching results have shown to be relatively better than OPAT-based results in this case. Here, Random Forests combined with FFD helped in improving the average error by 23.1%, along with the reduction of the standard deviation of errors by 28.9% relative to OPAT. Combining Random Forests with FFD is relatively better than OPAT because of the rigorous permutation tests of each parameter. In addition, this combination requires only 16 samples, where OPAT requires 21 samples. The combination of Random Forests and FFD as a sensitivity analysis tool successfully selected the top parameters which are horizontal permeability-related. Such parameters are well known in their direct relationship with water production as described by the well-known fractional flow theory. To conclude, this study found there is a strong indication that Random Forests can be a more reliable sensitivity analysis tool in production history matching, given the right configuration and design of experiments strategy.

Faster MapReduce Computation on Clouds Through Better Performance Estimation

Processing Big Data in cloud is on the increase. An important issue for efficient execution of Big Data processing jobs on a cloud platform is selecting the best fitting virtual machine (VM) configuration(s) among the miscellany of choices that cloud providers offer. Wise selection of VM configurations can lead to better performance, cost and energy consumption. Therefore, it is crucial to explore the available configurations and opt for the best ones that well suit each MapReduce application. Profiling the given application on all the configurations is costly, time and energy consuming. An alternative is to run the application on a subset of configurations (sample configurations) and estimate its performance on other configurations based on the obtained values by sample configurations. We show that the choice of these sample configurations highly affects accuracy of later estimations. Our Smart Configuration Selection (SCS) scheme chooses better representatives from among all configurations by once-off analysis of given performance figures of the benchmarks so as to increase the accuracy of estimations of missing values, and consequently, to more accurately choose the configuration providing the highest performance. The results show that the SCS choice of sample configurations is very close to the best choice, and can reduce estimation error to 11.58 percent from the original 19.72 percent of random configuration selection. More importantly, using SCS estimations in a makespan minimization algorithm improves the execution time by up to 36.03 percent compared with random sample selection.

The role of entropy in topological quantum error correction

The performance of a quantum error-correction process is determined by the likelihood that a random configuration of errors introduced to the system will lead to the corruption of encoded logical information. In this work we compare two different variants of the surface code with a comparable number of qubits: the surface code defined on a square lattice and the same model on a lattice that is rotated by . This seemingly innocuous change increases the distance of the code by a factor of . However, as we show, this gain can come at the expense of significantly increasing the number of different failure mechanisms that are likely to occur. We use a number of different methods to explore this tradeoff over a large range of parameter space under an independent and identically distributed noise model. We rigorously analyze the leading order performance for low error rates, where the larger distance code performs best for all system sizes. Using an analytical model and Monte Carlo sampling, we find that this improvement persists for fixed sub-threshold error rates and large system sizes, but that the improvement vanishes close to threshold. Remarkably, intensive numerics uncover a region of system sizes and sub-threshold error rates where the square lattice surface code marginally outperforms the rotated model.

ON THE MAXIMUM OF THE MODULARITY OF RANDOM CONFIGURATION GRAPHS

Configuration graphs with random independent identically distributed vertex degrees are considered. The degrees are equal to the number of vertex semiedges that are numbered in an arbitrary order. The graph is constructed by joining all of semiedges pairwise equiprobably to form edges. Such models can be used to adequately describe the topology of transport, electricity, social networks and the Internet. An important characteristic of the structure of a graph is its modularity. It is a measure for graph clustering in the case vertices are divided into groups (clusters). Graphs with high modularity have dense edges between the vertices within clusters but sparse connections between vertices of different clusters. The notion of modularity and its properties in random configuration graphs are discussed. The maximum modularity of a graph is used to describe the level of graph clustering and to find the best division of vertices. The limit theorem for the maximum modularity as the number of vertices tends to infinity is proved.

Polarization of Astrophysical Events with Precessing Jets

Abstract A central compact object (CCO; e.g., a black hole) with an accretion disk has been suggested as the common central engine of various astrophysical phenomena, such as gamma-ray bursts (GRBs), tidal disruption events (TDEs), and active galactic nuclei (AGNs). A jet powered by such a system might precess due to the misalignment of the angular momenta of the CCO and accretion disk. Some quasiperiodic behaviors observed in the light curves of these phenomena can be well interpreted within the framework of a precessing jet model. In this paper, we study the emission polarization of precessing jets in these three kinds of phenomena. The polarization angle also shows a gradual change for the synchrotron emission in both the random and toroidal magnetic field configurations with the precessing jet, while it can only change abruptly by 90° for the nonprecessing top-hat jet. Polarization properties are periodic due to the assumptions made in our model. The polarization observations are crucial to confirm the precession nature of jets in GRBs, TDEs, and AGNs.

Structure and High Performance of Lead-Free (K0.5Na0.5)NbO3 Piezoelectric Nanofibers with Surface-Induced Crystallization at Lowered Temperature.

Lead-free potassium and sodium niobate (KNN) nanofiber webs with random and aligned configurations were prepared by the electrospinning process from polymer-modified chemical solution. The crystallization process, structure, composition, dielectric, ferroelectric, and piezoelectric properties of the nanofibers and nanofiber webs were investigated. Theoretical analysis and experimental results showed that the surface-induced heterogeneous nucleation resulted in the remarkable lower crystallization temperature for the KNN nanofibers with the {100} orientation of the perovskite phase in contrast to the bulk KNN gel and thus well-controlled chemical stoichiometry. Low dielectric loss, large electric polarization, and high piezoelectric performance were obtained in the nanofiber webs. In particular, the aligned nanofiber web exhibited further improved piezoelectric strain and voltage coefficients and higher FOM than their thin film counterparts and is promising for high-performance electromechanical sensor and transducer applications.

Spherical Domain Rate-Distortion Optimization for Omnidirectional Video Coding

Efficient compression of omnidirectional video is important for emerging virtual reality applications. To compress this kind of video, each frame is projected to a 2D plane [e.g., equirectangular projection (ERP) map] first, adapting to the input format of existing video coding systems. At the display side, an inverse projection is applied to the reconstructed video to restore signals in spherical domain. Such a projection, however, makes presentation and encoding in different domains. Thus, an encoder agnostic to the projection performs inefficiently. In this paper, we analyze how a projection influences the distortion measurements in different domains. Based on the analysis, we propose a scheme to optimize the encoding process based on signals’ distortion in spherical domain. With the proposed optimization, an average 4.31% (up to 9.67%) luma BD-rate reduction is achieved for ERP in random access configuration. The corresponding bit saving is averagely 10.84% (up to 34.44%) when considering the viewing field being $\pi /2$ . The proposed method also benefits other projections and viewport settings, with a marginal complexity increase.

Fast Coding Unit Partition Decision for HEVC Using Support Vector Machines

Despite the several speedup methods proposed in the literature, the computational complexity of High Efficiency Video Coding (HEVC) video encoding is still a problem. This paper proposes a fast coding unit (CU) partition decision for use in HEVC encoders based on support vector machine (SVM)-trained offline. The SVM classifiers, features, and training procedures are described in detail, and a justification for the use of SVMs is provided. The trained classifiers are incorporated into a modified reference encoder in the form of a fast CU partition decision algorithm, which decides if the exhaustive search for the best partition is continued or terminated prematurely. Using the proposed method, an average complexity reduction of 48% is achieved with a 0.48% Bjontegaard-Delta bitrate (BD-BR) loss using the random access coding configuration, 44% reduction with a 0.62% BD-BR loss for the Low Delay B, and a 41% reduction with a 0.6% BD-BR loss for the Low Delay P configuration. We also tested our approach under constant bitrate conditions, achieving a 47% reduction in encoding time with a 1.11% loss in the BD-BR. In addition, a decision threshold adaptation is also proposed to allow adjusting the rate-distortion/complexity trade-off of our solution. With this approach, the computational complexity reduction can be varied from 34.9% (with a 0.13% loss in the BD-BR) up to 52.4% (with a 1.11% BD-BR loss) using the random access configuration. Compared with the state-of-the-art solutions, our decision scheme outperforms the related works in terms of combined rate distortion and complexity.

Continuous perception for deformable objects understanding

We present a robot vision approach to deformable object classification, with direct application to autonomous service robots. Our approach is based on the assumption that continuous perception provides robots with greater visual competence for deformable objects interpretation and classification. Our approach classifies the category of clothing items by continuously perceiving the dynamic interactions of the garment’s material and shape as it is being picked up. For this, we extract continuously visual features of a RGB-D video sequence and we fuse features by means of the Locality Constrained Group Sparse Representation (LGSR) algorithm. To evaluate the performance of our approach, we created a fully annotated database featuring 150 garment videos in random configurations. Experiments demonstrate that by continuously observing an object deform, our approach achieves a classification score of 66.7%, outperforming state-of-the-art approaches by a ∼27.3% increase.