Multi-resolution Model Research Articles

Varying the Resolution of the Rouse Model on Temporal and Spatial Scales: Application to Multiscale Modeling of DNA Dynamics

A multiresolution bead-spring model for polymer dynamics is developed as a generalization of the Rouse model. A polymer chain is described using beads of variable sizes connected by springs with variable spring constants. A numerical scheme which can use different timesteps to advance the positions of different beads is presented and analyzed. The position of a particular bead is updated only at integer multiples of the timesteps associated with its connecting springs. This approach extends the Rouse model to a multiresolution model on both spatial and temporal scales, allowing simulations of localized regions of a polymer chain with high spatial and temporal resolution, while using a coarser modeling approach to describe the rest of the polymer chain. A method for changing the model resolution on the fly is developed using the Metropolis--Hastings algorithm. It is shown that this approach maintains key statistics of the end-to-end distance and diffusion of the polymer filament and makes computational savings when applied to a model for the binding of a protein to the DNA filament.

Stratified drought analysis using a stochastic ensemble of simulated and in-situ soil moisture observations

This study proposes a multi-wavelet Bayesian ensemble of two Land Surface Models (LSMs) using in-situ observations for accurate estimation of soil moisture for Contiguous United States (CONUS). In the absence of a continuous, accurate in-situ soil moisture dataset at high spatial resolution, an ensemble of Noah and Mosaic LSMs is derived by performing a Bayesian Model Averaging (BMA) of several wavelet-based multi-resolution regression models (WR) of the simulated soil moisture from the LSMs and in-situ volumetric soil moisture dataset obtained from the U.S. Climate Reference Network (USCRN) field stations. This provides a proxy to the in-situ soil moisture dataset at 1/8th degree spatial resolution called Hybrid Soil Moisture (HSM) for three soil layers (1–10cm, 10–40cm and 40–100cm) for the CONUS. The derived HSM is used further to study the layer-wise response of soil moisture to drought, highlighting the necessity of the ensemble approach and soil profile perspective for drought analysis. A correlation analysis between HSM, the long-term (PDSI, PHDI, SPI-9, SPI-12 and SPI-24) and the short-term (Palmer Z index, SPI-1 and SPI-6) drought indices is carried out for the nine climate regions of the U.S. indicating a higher sensitivity of soil moisture to drought conditions for the Southern U.S. Furthermore, a layer-wise soil moisture percentile approach is proposed and applied for drought reconstruction in CONUS with a focus on the Southern U.S. for the year 2011.

Texture Segmentation Using Laplace Distribution-Based Wavelet-Domain Hidden Markov Tree Models

Multiresolution models such as the wavelet-domain hidden Markov tree (HMT) model provide a powerful approach for image modeling and processing because it captures the key features of the wavelet coefficients of real-world data. It is observed that the Laplace distribution is peakier in the center and has heavier tails compared with the Gaussian distribution. Thus we propose a new HMT model based on the two-state, zero-mean Laplace mixture model (LMM), the LMM-HMT, which provides significantly potential for characterizing real-world textures. By using the HMT segmentation framework, we develop LMM-HMT based segmentation methods for image textures and dynamic textures. The experimental results demonstrate the effectiveness of the introduced model and segmentation methods.

Application of multi-scale wavelet entropy and multi-resolution Volterra models for climatic downscaling

This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.’69 to Dec.’94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.’95–Dec.’05) and forecast (Jan.’06–Dec.’35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90–95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season.

Multi-resolution modeling based on quotient space and DEVS

As simulation systems get more and more complex, the study of multi-resolution modeling (MRM) remains an exciting and fertile area of research. Contrasting its abundant successful use cases, a rigorous mathematical foundation is still lacking in MRM. In this paper, we propose a quotient space based multi-resolution modeling (QMRM) theory based on granular computing in artificial intelligence and on discrete-event system specification (DEVS) in modeling and simulation. Based on quotient sets, resolution, multi-resolution modeling and other related concepts are defined and a general concept framework is constructed. Based on the concepts of quotient set and natural projection, several MRM principles are derived. The internal consistency principle guarantees consistency among different perspectives of an atomic model, whereas the external consistency principle guarantees that different components in a coupled model are consistent. The false-preserving principle indicates that if a construction relation or state transformation relation of a component does not exist in a low resolution model, then the corresponding relations should not exist in its high resolution model. The true-preserving principle tells us that a high resolution model can be simplified by choosing the proper low resolution model. QMRM is not only a formal specification, but also a fundamental framework to understand MRM concepts, a guiding ideology to design specific MRM methods, and a modeling methodology to develop MRM systems. QMRM is created from a general simulation perspective, not limited by any specific application or problem domain aspects. The results of this paper can serve as a starting point for further study of multi-resolution problems in different domains.

A multi-resolution model to capture both global fluctuations of an enzyme and molecular recognition in the ligand-binding site.

In multi-resolution simulations, different system components are simultaneously modeled at different levels of resolution, these being smoothly coupled together. In the case of enzyme systems, computationally expensive atomistic detail is needed in the active site to capture the chemistry of ligand binding. Global properties of the rest of the protein also play an essential role, determining the structure and fluctuations of the binding site; however, these can be modeled on a coarser level. Similarly, in the most computationally efficient scheme only the solvent hydrating the active site requires atomistic detail. We present a methodology to couple atomistic and coarse-grained protein models, while solvating the atomistic part of the protein in atomistic water. This allows a free choice of which protein and solvent degrees of freedom to include atomistically. This multi-resolution methodology can successfully model stable ligand binding, and we further confirm its validity by exploring the reproduction of system properties relevant to enzymatic function. In addition to a computational speedup, such an approach can allow the identification of the essential degrees of freedom playing a role in a given process, potentially yielding new insights into biomolecular function. Proteins 2016; 84:1902-1913. © 2016 Wiley Periodicals, Inc.

Validation of a predictive tool for the heading of turret-moored vessels

This paper presents the validation of tool which combines a model of the steady wind, current and wave loads with numerical estimates of the metocean conditions to predict the heading of a turret-moored vessel such as an FPSO. Estimates of the wave-induced mean drift contribution were obtained using an open-source three-dimensional boundary element code. Empirical data from reduced-scale tests were used to obtain the wind and current loads, which were shown to enable more reliable estimates than would be possible using data for very large crude carriers. The environmental conditions were predicted numerically using multi-resolution nested models which assimilate observed data. Using measurements from an operating FPSO for full-scale validation, the model was shown to be able to predict the heading for a range of environmental conditions including non-collinear seas, swells and winds to an accuracy typically within 5%. Preliminary estimates of the vessel roll amplitudes also agreed favourably with measured values. It is envisaged that the model will be attractive for operators of FPSOs in better facilitating the identification of dangerous conditions. This could ultimately lead to safer and more efficient operations.

Automatic visual inspection of a missing split pin in the China railway high-speed.

The split pin (SP) on the caliper brake is a vital component of the brake system of a bogie traveling along the China railway high-speed (CRH), and the absence of the SP could cause serious train accidents. A new automatic visual inspection method is proposed for the quick and accurate detection of SP faults of the CRH. The proposed approach is based on the histogram of gradient (HOG) combined with the complete local binary pattern (CLBP). First, a fast pyramid template matching technique is presented for localizing the region of interest to reduce the searching scope. Under the multiresolution pyramid model for target localization, a coarse-to-fine strategy is employed to ensure that the recognizing speed of the SP for the entire image is increased significantly. Second, a hierarchical framework is adopted at the localizing and inspecting stages of the SP to automatically implement the inspection tasks. To increase the robustness to the outside complex illumination, the HOG feature for localizing the target and the CLBP feature for examining the state of the SP (i.e., missing or not-missing) are extracted in the Sobel gradient domain. The localization and recognition stages are both fulfilled through the use of their respective intersection kernel support vector machine classifiers and corresponding features. In conclusion, experimental results indicate that the inspection system achieves a high accuracy rate of more than 99.0% and a real-time speed, thus proving that the proposed method is effective for the fault inspection of the SP and can satisfy the requirements of CRH's actual application.

Predicting speech intelligibility based on a correlation metric in the envelope power spectrum domain.

19(7), 2125-2136]. This "hybrid" model, named sEPSMcorr, is shown to account for the effects of stationary and fluctuating additive interferers as well as for the effects of non-linear distortions, such as spectral subtraction, phase jitter, and ideal time frequency segregation (ITFS). The model shows a broader predictive range than both the original mr-sEPSM (which fails in the phase-jitter and ITFS conditions) and STOI (which fails to predict the influence of fluctuating interferers), albeit with lower accuracy than the source models in some individual conditions. Similar to other models that employ a short-term correlation-based back end, including STOI, the proposed model fails to account for the effects of room reverberation on speech intelligibility. Overall, the model might be valuable for evaluating the effects of a large range of interferers and distortions on speech intelligibility, including consequences of hearing impairment and hearing-instrument signal processing.

Component-based multi-resolution dynamic structure modeling and simulations

Component-based multi-resolution dynamic structure modeling and simulations

Shape distribution-based retrieval of 3D CAD models at different levels of detail

With increasing design reuse in modern products, accurate and efficient 3D CAD model retrieval methods are required. To improve retrieval capability, a 3D shape comparison method is often utilized. In this method, the shapes of 3D CAD models in a database are compared with the shape given by a user. Meanwhile, for rapid generation of query models, a freehand sketch-based modeling method is adopted for retrieval systems. This method creates a low-level-of-detail (low-LOD) 3D CAD model with abbreviated exterior shapes. On the other hand, the target 3D CAD model in the database is a high-LOD 3D CAD model including detailed shapes of a product or components of a product. Considering different LODs of query and target models, we propose a new 3D CAD model retrieval method consisting of a 3D CAD model simplification system and a shape distribution-based shape comparison engine that compares multi-resolution models in a database to improve retrieval accuracy using a query model with simple shape. Experiment is conducted on 64 LOD models generated from 8 test cases and 8 query models generated by freehand sketch-based modelling method. Result shows a 200 % improvement on retrieval success rate for lower LOD models (100 %) compared with source models (50 %). Moreover, the proposed method has an advantage on efficiency, due to the simple calculation method and short computation time.

Analyzing gene expression time-courses based on multi-resolution shape mixture model

Objective:Biological processes actually are a dynamic molecular process over time. Time course gene expression experiments provide opportunities to explore patterns of gene expression change over a time and understand the dynamic behavior of gene expression, which is crucial for study on development and progression of biology and disease. Analysis of the gene expression time-course profiles has not been fully exploited so far. It is still a challenge problem. We propose a novel shape-based mixture model clustering method for gene expression time-course profiles to explore the significant gene groups.Results: Based on multi-resolution fractal features and mixture clustering model, we proposed a multi-resolution shape mixture model algorithm. Multi-resolution fractal features is computed by wavelet decomposition, which explore patterns of change over time of gene expression at different resolution. Our proposed multi-resolution shape mixture model algorithm is a probabilistic framework which offers a more natural and robust way of clustering time-course gene expression. We assessed the performance of our proposed algorithm using yeast time-course gene expression profiles compared with several popular clustering methods for gene expression profiles. The grouped genes identified by different methods are evaluated by enrichment analysis of biological pathways and known protein–protein interactions from experiment evidence. The grouped genes identified by our proposed algorithm have more strong biological significance.Conclusion: A novel multi-resolution shape mixture model algorithm based on multi-resolution fractal features is proposed. Our proposed model provides a novel horizons and an alternative tool for visualization and analysis of time-course gene expression profiles.Availability: The R and Matlab program is available upon the request.

Application of multi-resolution 3D techniques in crime scene documentation with bloodstain pattern analysis

In forensic documentation with bloodstain pattern analysis (BPA) it is highly desirable to obtain non-invasively overall documentation of a crime scene, but also register in high resolution single evidence objects, like bloodstains. In this study, we propose a hierarchical 3D scanning platform designed according to the top-down approach known from the traditional forensic photography. The overall 3D model of a scene is obtained via integration of laser scans registered from different positions. Some parts of a scene being particularly interesting are documented using midrange scanner, and the smallest details are added in the highest resolution as close-up scans. The scanning devices are controlled using developed software equipped with advanced algorithms for point cloud processing.To verify the feasibility and effectiveness of multi-resolution 3D scanning in crime scene documentation, our platform was applied to document a murder scene simulated by the BPA experts from the Central Forensic Laboratory of the Police R&D, Warsaw, Poland.Applying the 3D scanning platform proved beneficial in the documentation of a crime scene combined with BPA. The multi-resolution 3D model enables virtual exploration of a scene in a three-dimensional environment, distance measurement, and gives a more realistic preservation of the evidences together with their surroundings. Moreover, high-resolution close-up scans aligned in a 3D model can be used to analyze bloodstains revealed at the crime scene. The result of BPA such as trajectories, and the area of origin are visualized and analyzed in an accurate model of a scene. At this stage, a simplified approach considering the trajectory of blood drop as a straight line is applied. Although the 3D scanning platform offers a new quality of crime scene documentation with BPA, some of the limitations of the technique are also mentioned.

Robust and flexible landmarks detection for uncontrolled frontal faces in the wild

In this paper, we propose a robust facial landmarking scheme for frontal faces which can be applied on both controlled and uncontrolled environment. This scheme is based on improvement/extension of the tree-structured facial landmarking scheme proposed by Zhu and Ramanan. The whole system is divided into two main parts: face detection and face landmarking. In the face detection part, we proposed a Tree-structured Filter Model (TFM) combined with Viola and Jones face detector to significantly reduce the false positives while maintaining high accuracy. For the facial landmarking step, we improve the accuracy and the amount of the facial landmarks by readjusting the face structure to provide better geometrical information. Furthermore, we expand the face models into Multi-Resolution (MR) models with the adaptive landmark approach via landmark reduction to train the face models to be able to detect facial landmarks on face images with resolutions as low as 30x30 pixels. Our experiments show that our proposed approaches can improve the accuracy of facial landmark detection on both controlled and uncontrolled environment. Furthermore, they also show that our MR models are more robust on detecting facial components (eyebrows, eyes, nose, and mouth) on very small faces.

Spatial prediction of naturally occurring gamma radiation in Great Britain

Gamma radiation from natural sources is an important component of background radiation, and correlates with childhood leukaemia risk in Great Britain. The geographic variation of indoor gamma radiation dose-rates in Great Britain is explored using various geo-statistical methods. A multi-resolution Gaussian-process model using radial basis functions with 2, 4, or 8 components, is fitted via maximum likelihood, and a non-spatial model is also used, fitted by ordinary least squares. Because of the dataset size (N = 10,199), four other parametric spatial models are fitted by variogram-fitting. A randomly selected 70:30 split is used for fitting:validation. The models are evaluated based on their predictive performance as measured by Mean Absolute Error, Mean Squared Error, as well as Pearson correlation and rank-correlation between predicted and actual dose-rates. Each of the four parametric models (Matérn, Gaussian, Bessel, Spherical) fitted the empirical variogram well, and yielded similar predictions at >50 km separation, although with more substantial differences in predicted variograms at <50 km. The multi-resolution Gaussian-process model with 8 components had the best predictive accuracy among the models considered. The Spherical, Bessel, Matérn, Gaussian and ordinary least squares models had progressively worse predictive performance, the ordinary least squares model being particularly poor in this respect.

The role of approximate morphisms in multiresolution modeling: Can we relax the strict lumpability requirements?

To become the core development methodology of major defense-related simulation projects, multiresolution modeling needs more advances in basic theory and practice. In this note, we illustrate an approach to addressing issues relating to approximate morphisms (structure/behavior preservation relations) between pairs of models. Such issues include metrics for departure from strict lumpability, tying approximate satisfaction of structural conditions to the resulting behavioral error, and how to enhance the probability of creating good approximate morphisms by humans and artificial modelers.

Envelope and intensity based prediction of psychoacoustic masking and speech intelligibility.

Human auditory perception and speech intelligibility have been successfully described based on the two concepts of spectral masking and amplitude modulation (AM) masking. The power-spectrum model (PSM) [Patterson and Moore (1986). Frequency Selectivity in Hearing, pp. 123-177] accounts for effects of spectral masking and critical bandwidth, while the envelope power-spectrum model (EPSM) [Ewert and Dau (2000). J. Acoust. Soc. Am. 108, 1181-1196] has been successfully applied to AM masking and discrimination. Both models extract the long-term (envelope) power to calculate signal-to-noise ratios (SNR). Recently, the EPSM has been applied to speech intelligibility (SI) considering the short-term envelope SNR on various time scales (multi-resolution speech-based envelope power-spectrum model; mr-sEPSM) to account for SI in fluctuating noise [Jørgensen, Ewert, and Dau (2013). J. Acoust. Soc. Am. 134, 436-446]. Here, a generalized auditory model is suggested combining the classical PSM and the mr-sEPSM to jointly account for psychoacoustics and speech intelligibility. The model was extended to consider the local AM depth in conditions with slowly varying signal levels, and the relative role of long-term and short-term SNR was assessed. The suggested generalized power-spectrum model is shown to account for a large variety of psychoacoustic data and to predict speech intelligibility in various types of background noise.

Predicting binaural speech intelligibility using the signal-to-noise ratio in the envelope power spectrum domain.

This study proposes a binaural extension to the multi-resolution speech-based envelope power spectrum model (mr-sEPSM) [Jørgensen, Ewert, and Dau (2013). J. Acoust. Soc. Am. 134, 436-446]. It consists of a combination of better-ear (BE) and binaural unmasking processes, implemented as two monaural realizations of the mr-sEPSM combined with a short-term equalization-cancellation process, and uses the signal-to-noise ratio in the envelope domain (SNRenv) as the decision metric. The model requires only two parameters to be fitted per speech material and does not require an explicit frequency weighting. The model was validated against three data sets from the literature, which covered the following effects: the number of maskers, the masker types [speech-shaped noise (SSN), speech-modulated SSN, babble, and reversed speech], the masker(s) azimuths, reverberation on the target and masker, and the interaural time difference of the target and masker. The Pearson correlation coefficient between the simulated speech reception thresholds and the data across all experiments was 0.91. A model version that considered only BE processing performed similarly (correlation coefficient of 0.86) to the complete model, suggesting that BE processing could be considered sufficient to predict intelligibility in most realistic conditions.

Towards multi-resolution global climate modeling with ECHAM6-FESOM. Part II: climate variability

This study forms part II of two papers describing ECHAM6-FESOM, a newly established global climate model with a unique multi-resolution sea ice-ocean component. While part I deals with the model description and the mean climate state, here we examine the internal climate variability of the model under constant present-day (1990) conditions. We (1) assess the internal variations in the model in terms of objective variability performance indices, (2) analyze variations in global mean surface temperature and put them in context to variations in the observed record, with particular emphasis on the recent warming slowdown, (3) analyze and validate the most common atmospheric and oceanic variability patterns, (4) diagnose the potential predictability of various climate indices, and (5) put the multi-resolution approach to the test by comparing two setups that differ only in oceanic resolution in the equatorial belt, where one ocean mesh keeps the coarse ~1° resolution applied in the adjacent open-ocean regions and the other mesh is gradually refined to ~0.25°. Objective variability performance indices show that, in the considered setups, ECHAM6-FESOM performs overall favourably compared to five well-established climate models. Internal variations of the global mean surface temperature in the model are consistent with observed fluctuations and suggest that the recent warming slowdown can be explained as a once-in-one-hundred-years event caused by internal climate variability; periods of strong cooling in the model (‘hiatus’ analogs) are mainly associated with ENSO-related variability and to a lesser degree also to PDO shifts, with the AMO playing a minor role. Common atmospheric and oceanic variability patterns are simulated largely consistent with their real counterparts. Typical deficits also found in other models at similar resolutions remain, in particular too weak non-seasonal variability of SSTs over large parts of the ocean and episodic periods of almost absent deep-water formation in the Labrador Sea, resulting in overestimated North Atlantic SST variability. Concerning the influence of locally (isotropically) increased resolution, the ENSO pattern and index statistics improve significantly with higher resolution around the equator, illustrating the potential of the novel unstructured-mesh method for global climate modeling.

Reframing the influence of national culture with theory-based multi-resolution simulation models

Purpose For several decades, national culture has been described as having major influence over international business outcomes. Yet national culture has been framed often by vague terms and simplistic scales. The purpose of this paper is to explain why and how the influence of national culture should be reframed. Design/methodology/approach Review of literature concerned with causation in the behaviour of individuals and groups: anthropology, cognition, psychology, cross-cultural psychology, cultural psychology and cultural geography. Findings Within every nationality, and across international business, there is dynamic complexity of thought and action among individuals and groups. This derives from differences of genders, age, cultures, personality types and past experiences; the highly complex interactions between them; their commingling with common traits; and the varying influence of contextual factors. This dynamic complexity cannot be addressed by managers through use of vague simplistic conceptualizations of national culture. Practical implications As an alternative to vague simplistic conceptualizations, scientific theories, such as resource-based theory, knowledge-based view, contagion theories and social cognition theory, can be referred to in the formulation of multi-resolution simulation models. These models can enable managers to analyze dynamic complex international business scenarios, in terms of situation-specific variables. Originality/value The originality of this paper is that it provides a detailed explanation of why vague simplistic conceptualizations of national culture are of limited usefulness to managers of international business. The value of this paper is that it describes a practical alternative: theory-based multi-resolution simulation models.