Critical slowing down and hyperuniformity on approach to jamming

Hyperuniformity characterizes a state of matter that is poised at a critical point at which density or volume-fraction fluctuations are anomalously suppressed at infinite wavelengths. Recently, much attention has been given to the link between strict jamming and hyperuniformity in frictionless hard-particle packings. Doing so requires one to study very large packings, which can be difficult to jam properly. We modify the rigorous linear programming method of Donev et al. [J. Comp. Phys. 197, 139 (2004)] in order to test for jamming in putatively jammed packings of hard-disks in two dimensions. We find that various standard packing protocols struggle to reliably create packings that are jammed for even modest system sizes; importantly, these packings appear to be jammed by conventional tests. We present evidence that suggests that deviations from hyperuniformity in putative maximally random jammed (MRJ) packings can in part be explained by a shortcoming in generating exactly-jammed configurations due to a type of "critical slowing down" as the necessary rearrangements become difficult to realize by numerical protocols. Additionally, various protocols are able to produce packings exhibiting hyperuniformity to different extents, but this is because certain protocols are better able to approach exactly-jammed configurations. Nonetheless, while one should not generally expect exact hyperuniformity for disordered packings with rattlers, we find that when jamming is ensured, our packings are very nearly hyperuniform, and deviations from hyperuniformity correlate with an inability to ensure jamming, suggesting that strict jamming and hyperuniformity are indeed linked. This raises the possibility that the ideal MRJ packings have no rattlers. Our work provides the impetus for the development of packing algorithms that produce large disordered strictly jammed packings that are rattler-free.Comment: 15 pages, 11 figures. Accepted for publication in Phys. Rev.

Explosions in Electrical Control Boxes as a Potential “Nested Bang-Box” Mechanism for Severe Vapour Cloud Explosions

The ignition source for Buncefield, the United Kingdom’s most severe recent vapour cloud explosion (VCE) was potentially electrical control boxes situated inside a pump house immersed in the vapour cloud. There are other reports of confined or bang box ignition sources for other VCEs, such as Port Hudson and Jaipur where it is proposed these ignition sources were responsible for transition to detonation (DDT). There has, however, been relatively little previous research into this type of ignition mechanism and its effect on the explosion severity. Commercially available electrical control boxes measuring 600 mm high, 400 mm wide and 250 mm deep were used to explore the pressure development, venting processes and flame characteristics of stoichiometric propane/air explosions using cling film, aluminium foil and the supplied doors as vent coverings. In this work the boxes were empty of their usual contents in order to establish a baseline for the effect of the internal congestion of the boxes. It was found that, in these empty-box tests the overpressure was dominated by the bursting pressure of the ventcovering and the external explosion, although clearly presenting significant ignition source to a potential surrounding flammable cloud, it produced no significant overpressure. The door produced a flat petal shaped flame that differed drastically from the rolling vortex bubble flame shape traditionally associated with vented explosions

Geoscience-aware deep learning:A new paradigm for remote sensing

Information extraction is a key activity for remote sensing images. A common distinction exists between knowledge-driven and data-driven methods. Knowledge-driven methods have advanced reasoning ability and interpretability, but have difficulty in handling complicated tasks since prior knowledge is usually limited when facing the highly complex spatial patterns and geoscience phenomena found in reality. Data-driven models, especially those emerging in machine learning (ML) and deep learning (DL), have achieved substantial progress in geoscience and remote sensing applications. Although DL models have powerful feature learning and representation capabilities, traditional DL has inherent problems including working as a black box and generally requiring a large number of labeled training data. The focus of this paper is on methods that integrate domain knowledge, such as geoscience knowledge and geoscience features (GK/GFs), into the design of DL models. The paper introduces the new paradigm of geoscience-aware deep learning (GADL), in which GK/GFs and DL models are combined deeply to extract information from remote sensing data. It first provides a comprehensive summary of GK/GFs used in GADL, which forms the basis for subsequent integration of GK/GFs with DL models. This is followed by a taxonomy of approaches for integrating GK/GFs with DL models. Several approaches are detailed using illustrative examples. Challenges and research prospects in GADL are then discussed. Developing more novel and advanced methods in GADL is expected to become the prevailing trend in advancing remotely sensed information extraction in the future.</p

Toward automated evaluation of interactive segmentation

We previously described a system for evaluating interactive segmentation by means of user experiments (McGuinness and O’Connor, 2010). This method, while effective, is time-consuming and labor-intensive. This paper aims to make evaluation more practicable by investigating if it is feasible to automate user interactions. To this end, we propose a general algorithm for driving the segmentation that uses the ground truth and current segmentation error to automatically simulate user interactions. We investigate four strategies for selecting which pixels will form the next interaction. The first of these is a simple, deterministic strategy; the remaining three strategies are probabilistic, and focus on more realistically approximating a real user. We evaluate four interactive segmentation algorithms using these strategies, and compare the results with our previous user experiment-based evaluation. The results show that automated evaluation is both feasible and useful

Spatial Association from the Perspective of Mutual Information

Measures of spatial association are important to reveal the spatial structures and patterns in geographical phenomena. They have utility for spatial interpolation, stochastic simulation, and causal inference, among others. Such measures are abundantly available for continuous spatial variables, whereas for categorical spatial variables they are less well developed. In this research, we developed a measure of spatial association for categorical spatial variables coined the entropogram, quantifying its spatial association using mutual information. Mutual information concerns information shared by pairs of random variables at different locations as revealed by their observed joint frequency distribution and marginal frequency distributions. The developed new measure is modeled as a function of lag in analogy to the variogram. Whereas existing measures focus mainly on interstate relationships, the entropogram models the spatial correlation in categorical spatial variables holistically. In this way, the entropogram imparts multiple advantages, for example, simplifying the representation of spatial structure for categorical variables and facilitating communication. The entropogram also reflects variation in the spatial correlation between different states. We first explored the properties of the entropogram in a simulation study. Then, we applied the entropogram to analyze the spatial association of land cover types in Qinxian, Shanxi, China. We conclude that the entropogram provides a suitable addition to existing measures of spatial association for applications in a wide range of disciplines where the categorical spatial variable is of interest.</p

Fourth-order compact schemes for solving multidimensional heat problems with Neumann boundary conditions

In this article, two sets of fourth-order compact finite difference schemes are constructed for solving heat-conducting problems of two or three dimensions, respectively. Both problems are with Neumann boundary conditions. These works are extensions of our earlier work (Zhao et al., Fourth order compact schemes of a heat conduction problem with Neumann boundary conditions, Numerical Methods Partial Differential Equations, to appear) for the one-dimensional case. The local one-dimensional method is employed to construct these two sets of schemes, which are proved to be globally solvable, unconditionally stable, and convergent. Numerical examples are also provided. © 2007 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2007Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/57369/1/20255_ftp.pd

Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016

Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) HH and HV polarization data were used previously to produce annual, global 25 m forest maps between 2007 and 2010, and the latest global forest maps of 2015 and 2016 were produced by using the ALOS-2 PALSAR-2 data. However, annual 25 m spatial resolution forest maps during 2011–2014 are missing because of the gap in operation between ALOS and ALOS-2, preventing the construction of a continuous, fine resolution time-series dataset on the world's forests. In contrast, the MODerate Resolution Imaging Spectroradiometer (MODIS) NDVI images were available globally since 2000. This research developed a novel method to produce annual 25 m forest maps during 2007–2016 by fusing the fine spatial resolution, but asynchronous PALSAR/PALSAR-2 with coarse spatial resolution, but synchronous MODIS NDVI data, thus, filling the four-year gap in the ALOS and ALOS-2 time-series, as well as enhancing the existing mapping activity. The method was developed concentrating on two key objectives: 1) producing more accurate 25 m forest maps by integrating PALSAR/PALSAR-2 and MODIS NDVI data during 2007–2010 and 2015–2016; 2) reconstructing annual 25 m forest maps from time-series MODIS NDVI images during 2011–2014. Specifically, a decision tree classification was developed for forest mapping based on both the PALSAR/PALSAR-2 and MODIS NDVI data, and a new spatial-temporal super-resolution mapping was proposed to reconstruct the 25 m forest maps from time-series MODIS NDVI images. Three study sites including Paraguay, the USA and Russia were chosen, as they represent the world's three main forest types: tropical forest, temperate broadleaf and mixed forest, and boreal conifer forest, respectively. Compared with traditional methods, the proposed approach produced the most accurate continuous time-series of fine spatial resolution forest maps both visually and quantitatively. For the forest maps during 2007–2010 and 2015–2016, the results had greater overall accuracy values (>98%) than those of the original JAXA forest product. For the reconstructed 25 m forest maps during 2011–2014, the increases in classifications accuracy relative to three benchmark methods were statistically significant, and the overall accuracy values of the three study sites were almost universally >92%. The proposed approach, therefore, has great potential to support the production of annual 25 m forest maps by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016

An easterly tip jet off Cape Farewell, Greenland. I: Aircraft observations

An easterly tip jet event off Cape Farewell, Greenland, is described and analysed in considerable detail. In Part I of this study (this paper) comprehensive aircraft-based observations are described, while in Part II of this study numerical simulations and a dynamical analysis are presented. The easterly tip jet of 21 February 2007 took place during the Greenland Flow Distortion experiment. It resulted through the interaction of a barotropic synoptic-scale low pressure system in the central North Atlantic and the high topography of southern Greenland. In situ observations reveal a jet core at the coast with peak winds of almost 50 m s-1, about 600–800 m above the sea surface, and of 30 m s-1 at 10 m. The depth of the jet increased with wind speed from ~1500 m to ~2500 m as the peak winds increased from 30 to 50 m s-1. The jet accelerated and curved anticyclonically as it reached Cape Farewell and the end of the barrier. The easterly tip jet was associated with a tongue of cold and dry air along the coast of southeast Greenland, general cloud cover to the east, and cloud streets to the south of Cape Farewell. Precipitation was observed during the low-level components of the flight. The very high wind speeds generated a highly turbulent atmospheric boundary layer and resulted in some of the highest surface wind stresses ever observed over the ocean

Impervious surface change mapping with an uncertainty-based spatial-temporal consistency model: a case study in Wuhan city using Landsat time-series datasets from 1987 to 2016

Detailed information on the spatial-temporal change of impervious surfaces is important for quantifying the effects of rapid urbanization. Free access of the Landsat archive provides new opportunities for impervious surface mapping with fine spatial and temporal resolution. To improve the classification accuracy, a temporal consistency (TC) model may be applied on the original classification results of Landsat time-series datasets. However, existing TC models only use class labels, and ignore the uncertainty of classification during the process. In this study, an uncertainty-based spatial-temporal consistency (USTC) model was proposed to improve the accuracy of the long time series of impervious surface classifications. In contrast to existing TC methods, the proposed USTC model integrates classification uncertainty with the spatial-temporal context information to better describe the spatial-temporal consistency for the long time-series datasets. The proposed USTC model was used to obtain an annual map of impervious surfaces in Wuhan city with Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI) images from 1987 to 2016. The impervious surfaces mapped by the proposed USTC model were compared with those produced by the support vector machine (SVM) classifier and the TC model. The accuracy comparison of these results indicated that the proposed USTC model had the best performance in terms of classification accuracy. The increase of overall accuracy was about 4.23% compared with the SVM classifier, and about 1.79% compared with the TC model, which indicates the effectiveness of the proposed USTC model in mapping impervious surfaces from long-term Landsat sensor imagery

Fast and Slow Changes Constrained Spatio-temporal Subpixel Mapping

Subpixel mapping (SPM) is a technique to tackle the mixed pixel problem and produce land cover and land use (LCLU) maps at a finer spatial resolution than the original coarse data. However, uncertainty exists unavoidably in SPM, which is an ill-posed downscaling problem. Spatio-temporal SPM methods have been proposed to deal with this uncertainty, but current methods fail to explore fully the information in the time-series images, especially more rapid changes over a short-time interval. In this paper, a fast and slow changes constrained spatio-temporal subpixel mapping (FSSTSPM) method is proposed to account for fast LCLU changes over a short-time interval and slow changes over a long-time interval. Namely, both fast and slow change-based temporal constraints are proposed and incorporated simultaneously into the FSSTSPM to increase the accuracy of SPM. The proposed FSSTSPM method was validated using two synthetic datasets with various proportion errors. It was also applied to oil-spill mapping using a real PlanetScope-Sentinel-2 dataset and Amazon deforestation mapping using a real Landsat-MODIS dataset. The results demonstrate the superiority of FSSTSPM. Moreover, the advantage of FSSTSPM is more obvious with an increase in proportion errors. The concepts of the fast and slow changes, together with the derived temporal constraints, provide a new insight to enhance SPM by taking fuller advantage of the temporal information in the available time-series images