Function Of Spatial Distance Research Articles

Bayesian optimized deep Q-network for diagnosing mine ventilation systems windage alteration fault targeting imbalanced data

Fault diagnosis of mine ventilation system is of great significance for mine safety production. Traditional machine learning algorithms have been widely applied in the field of mine ventilation systems windage alteration faults (WAFs) diagnosis, but these algorithms have poor intelligence and weak generalization ability. Therefore, this research constructs a WAFs diagnosis model (WAFs-BODQN) based on Bayesian optimized (BO) deep Q-network (DQN). Meanwhile, in order to solve the problem of poor performance of intelligent models when the dataset is imbalanced, a reinforcement learning reward function (KMeans-SDF) was designed, which combines the KMeans++ algorithm and spatial distance function (SDF). The WAFs-BODQN model with KMeans-SDF reward function has fault location diagnosis accuracies of 98.75 %, 97.50 %, and 95.00 % for mild, moderate, and extreme imbalanced datasets UB1, UB2, and UB3, respectively. This effectively avoids the "eccentricity" phenomenon of the intelligent model on majority class and achieves accurate prediction of fault locations. The accuracy of fault location diagnosis using the WAFs-BODQN model in simulation experiments and on-site empirical applications is higher than 95 %, demonstrating the intelligence, generalization, and feasibility of WAFs-BODQN model in the field of WAFs diagnosis in mine ventilation systems. By comparing the results with traditional machine learning and deep learning fault diagnosis models, it has been proven that the WAFs-BODQN model has advantages in intelligence and generalization ability. It is expected to establish a universal architecture for diagnosing WAFs, providing theoretical guidance and technical support for achieving intelligent mine ventilation.

Spectral dimension on spatial hypersurfaces in causal set quantum gravity

An important probe of quantum geometry is its spectral dimension, defined via a spatial diffusion process. In this work we study the spectral dimension of a ‘spatial hypersurface’ in a manifoldlike causal set using the induced spatial distance function. In previous work, the diffusion was taken on the full causal set, where the nearest neighbours are unbounded in number. The resulting super-diffusion leads to an increase in the spectral dimension at short diffusion times, in contrast to other approaches to quantum gravity. In the current work, by using a temporal localisation in the causal set, the number of nearest spatial neighbours is rendered finite. Using numerical simulations of causal sets obtained from d = 3 Minkowski spacetime, we find that for a flat spatial hypersurface, the spectral dimension agrees with the Hausdorff dimension at intermediate scales, but shows clear indications of dimensional reduction at small scales, i.e. in the ultraviolet. The latter is a direct consequence of ‘discrete asymptotic silence’ at small scales in causal sets.

How important is patch size relative to patch isolation in an arid inselberg landscape?

Patch size (area) and isolation (inverse distance to source) are key variables of island biogeography, but their application in conservation planning has recently come under scrutiny. Based on animal distribution data in fragmented landscapes from around the world it was postulated that occupancy was poorly predicted by patch size and isolation. Do these findings also apply to arid areas and to plants?This question was investigated in four inselberg landscapes in the Namib Desert stretching over a 770 km climatic north–south gradient. Inselbergs (isolated mountains) are special habitats and can be considered ‘islands’ of more mesic conditions in an arid matrix. Species richness of inselberg specialists was used as an indicator and regressed against area, distance to nearest habitat and distance to ‘mainland’ (or source pool, which are proxies for level of isolation). Additionally, the influence of landscape geographic position, land use and rock types were studied. Responses of different life forms and individual plant species as well as possible dispersal limitations were also investigated.Whether patch size is more important than isolation in determining species richness on arid inselbergs could not be answered at a landscape level. However, when investigated on a functional and species level, isolation was of greater importance than patch size for trees and also for selected species such as Commiphora saxicola and Heliotropium steudneri. The effect of distance to source in this study is likely a function of spatial distance as well as a coast-inland moisture gradient. Inselberg specialist-richness was not affected by land use or the bioclimatic position. Yet rock type, which is a reflection of soil physical and chemical properties did affect species richness with granites and basalts supporting more species. Life forms of inselberg specialists were influenced by the investigated environmental variables, but dispersal limitations were not. Despite inconclusive results at a landscape level, functional groups and individual species provided more conclusive results. This points towards the necessity of additional investigations at a finer scale to gain an understanding of the factors driving species distributions. In future more detail regarding the ‘matrix’ surrounding ‘patches’, as well as evolutionary and vegetation historic factors should be explored. Phylogenetic studies would be one tool to help unravelling some of the presently unknown factors affecting species distributions on Namib inselbergs. This study also highlighted the importance of inselbergs in arid landscapes as ‘hotspots’ of species richness and species of conservation importance.

Steps towards Lorentzian quantum gravity with causal sets

Causal set quantum gravity is a Lorentzian approach to quantum gravity, based on the causal structure of spacetime. It models each spacetime configuration as a discrete causal network of spacetime points. As such, key questions of the approach include whether and how a reconstruction of a sufficiently coarse-grained spacetime geometry is possible from a causal set. As an example for the recovery of spatial geometry from discrete causal structure, the construction of a spatial distance function for causal sets is reviewed. Secondly, it is an open question whether the path sum over all causal sets gives rise to an expectation value for the causal set that corresponds to a cosmologically viable spacetime. To provide a tool to tackle the path sum over causal sets, the derivation of a flow equation for the effective action for causal sets in matrix-model language is reviewed. This could provide a way to coarse-grain discrete networks in a background-independent way. Finally, a short roadmap to test the asymptotic-safety conjecture in Lorentzian quantum gravity using causal sets is sketched.

Induced spatial geometry from causal structure

Motivated by the Hawking–King–McCarthy–Malament (HKMM) theorem and the associated reconstruction of spacetime geometry from its causal structure and local volume element , we define a one-parameter family of spatial distance functions on a Cauchy hypersurface using only and . The parameter corresponds to a ‘mesoscale’ cut-off which, when appropriately chosen, provides a distance function which approximates the induced spatial distance function to leading order. This admits a straightforward generalisation to the discrete analogue of a Cauchy hypersurface in a causal set. For causal sets which are approximated by continuum spacetimes, this distance function approaches the continuum induced distance when the mesoscale is much smaller than the scale of the extrinsic curvature of the hypersurface, but much larger than the discreteness scale. We verify these expectations by performing extensive numerical simulations of causal sets which are approximated by simple spacetime regions in 2 and 3 spacetime dimensions.

Exceedance-based nonlinear regression of tail dependence

The probability and structure of co-occurrences of extreme values in multivariate data may critically depend on auxiliary information provided by covariates. In this contribution, we develop a flexible generalized additive modeling framework based on high threshold exceedances for estimating covariate-dependent joint tail characteristics for regimes of asymptotic dependence and asymptotic independence. The framework is based on suitably defined marginal pretransformations and projections of the random vector along the directions of the unit simplex, which lead to convenient univariate representations of multivariate exceedances based on the exponential distribution. Good performance of our estimators of a nonparametrically designed influence of covariates on extremal coefficients and tail dependence coefficients are shown through a simulation study. We illustrate the usefulness of our modeling framework on a large dataset of nitrogen dioxide measurements recorded in France between 1999 and 2012, where we use the generalized additive framework for modeling marginal distributions and tail dependence in large concentrations observed at pairs of stations. Our results imply asymptotic independence of data observed at different stations, and we find that the estimated coefficients of tail dependence decrease as a function of spatial distance and show distinct patterns for different years and for different types of stations (traffic vs. background).

Decorrelation scales for Arctic Ocean hydrography – Part I: Amerasian Basin

Abstract. Any use of observational data for data assimilation requires adequate information of their representativeness in space and time. This is particularly important for sparse, non-synoptic data, which comprise the bulk of oceanic in situ observations in the Arctic. To quantify spatial and temporal scales of temperature and salinity variations, we estimate the autocorrelation function and associated decorrelation scales for the Amerasian Basin of the Arctic Ocean. For this purpose, we compile historical measurements from 1980 to 2015. Assuming spatial and temporal homogeneity of the decorrelation scale in the basin interior (abyssal plain area), we calculate autocorrelations as a function of spatial distance and temporal lag. The examination of the functional form of autocorrelation in each depth range reveals that the autocorrelation is well described by a Gaussian function in space and time. We derive decorrelation scales of 150–200 km in space and 100–300 days in time. These scales are directly applicable to quantify the representation error, which is essential for use of ocean in situ measurements in data assimilation. We also describe how the estimated autocorrelation function and decorrelation scale should be applied for cost function calculation in a data assimilation system.

Chimeric Synergy in Natural Social Groups of a Cooperative Microbe

Many cooperative species form internally diverse social groups in which individual fitness depends significantly on group-level productivity from cooperation [1-4]. For such species, selection is expected to often disfavor within-group diversity that reduces cooperative productivity [5, 6]. While diversity within social groups is known to enhance productivity in some animals [7-9], diversity within natural groups of social microbes is largely unexamined in this regard. Cells of the soil bacterium Myxococcus xanthus respond to starvation by constructing multicellular fruiting bodies within each of which a subpopulation of cells transforms into stress-resistant spores [10]. Fruiting bodies isolated from soil often harbor substantial endemic diversity [11] that is, nonetheless, lower than between-group diversity, which increases with distance from millimeter to global scales [12-14]. We show that M.xanthus clones isolated from the same fruiting body often collectively produce more viable spores in chimeric groups than expected from sporulation in genetically homogeneous groups. In contrast, chimerism among clones derived from different fruiting bodies tends toreduce group productivity, and it does so increasingly as a function of spatial distance between fruiting-body sample sites. For one fruiting body examined in detail, chimeric synergy-a positive quantitative effect of chimerism on group productivity-is distributed broadly across an interaction network rather than limited to a few interactions. We propose that these results strengthen the plausibility of the hypothesis that selection may operate not only within Myxococcus groups, but also between kin groups to disfavor within-group variation that reduces productivity while allowing some forms of diversity that generate chimeric synergy to persist.

The economic impact of agricultural pollutions in Iran, spatial distance function approach

Pollution has increased in Iran in the last two decades due to agricultural activities. Pollutions are released into the atmosphere and thereafter pollute water and soil resources. Therefore, it is important to develop models that can estimate the economic value of these Pollutions. For this purpose, a Spatial Output Distance Function was used in the present study to estimate the shadow price of carbon dioxide emission and water salinity in spatial and temporal dimensions. This method can be used as a new method to estimate the value of externalities. In this study, the data of provinces in Iran during 1995–2014 were used. The results showed that the total shadow price of agricultural Pollutions was equal to 42.54 dollars per unit. In addition, the effects due to the adjacency of regions to each other led to the transfer of pollution and its externalities were equal to 26.3 dollars per unit of pollution. This finding refers to the importance of considering the spatial dimension when analyzing the shadow price of pollutions caused by production. Thus, internalizing the damage caused by pollutions based on the adjacency of regions to each other can increase the efficiency of the production environment.

Synaptic convergence regulates synchronization-dependent spike transfer in feedforward neural networks

Correlated neural activities such as synchronizations can significantly alter the characteristics of spike transfer between neural layers. However, it is not clear how this synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. To address this question, we implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a constant value or an exponentially decaying function, respectively. Then we examined how the spike transfer function is modulated under these conditions, while static or synchronized input patterns were introduced to simulate different levels of feedforward spike synchronization. We observed that the synchronization-dependent modulation of the transfer function appeared noticeably different for each convergence condition. The modulation of the spike transfer function was largest in the UC model, and smallest in the UE model. Our analysis showed that this difference was induced by the different spike weight distributions that was generated from convergent synapses in each model. Our results suggest that, the structure of the feedforward convergence is a crucial factor for correlation-dependent spike control, thus must be considered important to understand the mechanism of information transfer in the brain.

Proof of concept: a spatial modular small-world self-organises by adaptive rewiring

A small-world network is a network that reconciles two opposing properties, segregation and integration. It is this reconciliation that gives rise to the impressive information processing capacity of the human brain; segregation provides a platform for information processing, whilst integration provides for the fast transmission of information. However, the connectivity structure of the brain is not static [1]; it changes on multiple time-scales; on a relatively fast time-scale, synaptic plasticity takes place, whilst on a slower time-scale there is rewiring of brain connectivity through growth of axons and dendrites. This structural plasticity depends on the even faster time-scale of neural activity. But the relationship is symbiotic: patterns of synchronous activity are, of necessity, mediated by the brain connectivity structure. Gong & van Leeuwen [2] showed that rewiring of an initially random network - adaptive rewiring - in a model of spontaneous cortical activity gives rise to a particular type of network connectivity structure: a modular small-world. In order to improve the applicability of such a model to the cortex, spatial characteristics of cortical connectivity need to be respected. For this purpose we consider networks endowed with a metric by embedding them into a physical space. Such spatial constraints may represent wiring and metabolic costs in the brain. We provide an adaptive rewiring model with a spatial distance function and a corresponding spatially local rewiring bias [3]. Figure 1 A, Network adjacency matrix organised to optimise visual presentation of modular structure. B, Units on the sphere colour-coded to identify distinct modules.

Experimental study of micro-spalling fragmentation from melted lead

In this paper, we conduct an experimental investigation of a Pb target shocked using high explosives through the use of an improved, non-radiographic diagnostic technique called an Asay window. In the case considered, the Pb sample is melted upon release at a shock-breakout pressure of about 38 GPa. By converting the measured velocity profile of an Asay window, the mass and density distributions as a function of spatial distance in the melted Pb sample were quantitatively obtained at different offsets, corresponding with the formation of micro-spalling fragments. The physical analysis is in reasonable agreement with the result for an Sn sample recorded by proton radiography.

Spatially constrained adaptive rewiring in cortical networks creates spatially modular small world architectures.

A modular small-world topology in functional and anatomical networks of the cortex is eminently suitable as an information processing architecture. This structure was shown in model studies to arise adaptively; it emerges through rewiring of network connections according to patterns of synchrony in ongoing oscillatory neural activity. However, in order to improve the applicability of such models to the cortex, spatial characteristics of cortical connectivity need to be respected, which were previously neglected. For this purpose we consider networks endowed with a metric by embedding them into a physical space. We provide an adaptive rewiring model with a spatial distance function and a corresponding spatially local rewiring bias. The spatially constrained adaptive rewiring principle is able to steer the evolving network topology to small world status, even more consistently so than without spatial constraints. Locally biased adaptive rewiring results in a spatial layout of the connectivity structure, in which topologically segregated modules correspond to spatially segregated regions, and these regions are linked by long-range connections. The principle of locally biased adaptive rewiring, thus, may explain both the topological connectivity structure and spatial distribution of connections between neuronal units in a large-scale cortical architecture.

Non-wadeable river bioassessment: spatial variation of benthic diatom assemblages in Pacific Northwest rivers, USA

Current bioassessment efforts are focused on small wadeable streams, at least partly because assessing ecological conditions in non-wadeable large rivers poses many additional challenges. In this study, we sampled 20 sites in each of seven large rivers in the Pacific Northwest, USA, to characterize variation of benthic diatom assemblages among and within rivers relative to environmental conditions. Analysis of sim- ilarity (ANOSIM) indicated that diatom assemblages were significantly different among all the seven rivers draining different ecoregions. Longitudinal patterns in diatom assemblages showed river-specific features. Bray-Curtis dissimilarity index values did not increase as a function of spatial distance among the sampled reaches within any river but the Malheur. Standardized Mantel r of association between assemblage similarity and spatial distance among sites ranged from a high of 0.69 (Malheur) to a low of 0.18 (Chehalis). In the Malheur River, % monoraphids, nitrogen-tolerant taxa, and beta-mesosaprobous taxa all decreased longitudi- nally while % motile taxa, especially Nitzschia ,s howed an opposite trend, reflecting a strong in-stream water quality gradient. Similar longitudinal trends in water quality were observed in other rivers but benthic diatom assemblages showed either weak response patterns or no patterns. Our study indicated that benthic diatom assemblages can clearly reflect among-river factors. The relationships between benthic diatom assemblages and water quality within each river may depend on the strength of the water quality gradients, interactive effects of water quality and habitat conditions, and diatom sampling design.

Fault-induced wetland loss at Matagorda, Texas, USA: land cover changes from 1943 to 2008

Movement of growth faults, a type of normal fault which is formed during sedimentation and is characterized by having greater vertical thickness on the downthrown fault side, on barrier islands contributes to wetland losses. This study sought to quantify land cover changes in response to vertical fault movement on the East Matagorda Peninsula barrier feature, Texas, USA. A time series of aerial images was classified using automated unsupervised classification and hand digitization. After classification, total wetland losses on both the upthrown and downthrown sides of the fault were evaluated as a function of spatial distance from the fault plane. Results show that while wetland loss occurred on both sides of the fault, losses were far more extensive on the downthrown side. It was concluded that this vertical fault movement impacts wetland losses, especially on the downthrown side.

Quantifying the influence of environmental texture on the rate of species turnover: evidence from two habitats

The environmental texture hypothesis (ETH) proposes that the spatial geometry or texture of the environment influences the rate at which species are accumulated in space or time. Specifically, the ETH suggests that regions, and spatial scales, that exhibit a larger rate of environmental distance decay (DD) should exhibit more rapid rates of species turnover. The ETH should apply over any range of scales where the environment is driving species distributions. To examine the relevance of the ETH at local spatial scales, we tested for a positive relationship between the rate of change in soil chemical properties and vascular plant species composition in grassland and woodland habitats. We recorded presence–absence data along a 1.883 km transect in each habitat and estimated the rate of turnover and environmental DD for spatial lags of 1–41 m. We found that the soil environment explained spatial patterns of species composition more accurately in the grassland habitat compared to the woodland habitat. Consequently the rate of change in soil properties as a function of spatial distance was significantly positively correlated with the rate of species turnover in the grassland but not the woodland. Our study suggests that one of the central premises of the ETH is relevant for local patterns of species turnover if the environment appears to influence species composition.

Depth propagation across an illusory surface

We investigate the spatiotemporal dynamics of depth filling in on an illusory surface by measuring the temporal asynchrony of perceived depth between an illusory neon-colored surface and real contours. We temporally modulated the horizontal disparity at vertical edges of the illusory surface and measured the perceptual delay for the interpolated surface's depth under two different boundary conditions: disparity given at both sides, or disparity given at one side and a free boundary at the other side. The results showed that the amount of the delay depends on the spatial distance between the measured point and the edges where disparity was physically given. Importantly, the observed delay as a function of spatial distance was clearly different under the two boundary conditions. We found that this difference can be fairly well explained by a model based on a diffusion equation under different boundary conditions. These results support the existence of locally represented depth information and an interpolation process based on mutual interaction of this information.

Interhelical Angle and Distance Preferences in Globular Proteins

Orientational preferences between interacting helices within globular proteins have been studied extensively over the years. A number of classical structural models such as “knobs into holes” and “ridges into grooves” were developed decades ago to explain perceived preferences in interhelical angle distributions. In contrast, relatively recent works have examined statistical biases in angular distributions which result from spherical geometric effects. Those works have concluded that the predictions of classical models are due in large part to these biases. In this article we perform an analysis on the largest set of helix-helix interactions within high-resolution structures of nonhomologous proteins studied to date. We examine the interhelical angle distribution as a function of spatial distance between helix pairs. We show that previous efforts to normalize angle distribution data did not include two important effects: 1), helices can interact with each other in three distinct ways which we refer to as “line-on-line,” “endpoint-to-line,” and “endpoint-to-endpoint,” and each of these interactions has its own geometric effects which must be included in the proper normalization of data; and 2), all normalizations that depend on geometric parameters such as interhelical angle must occur before the data is binned to avoid artifacts of bin size from biasing the conclusions. Taking these two points into account, we find that there are very pronounced preferences for helices to interact at angles of approximately ±160 and ±20° in the line-on-line case. This pattern persists when the closest α-carbons in the helices vary from 4 to 12 Å. The endpoint-to-line and endpoint-to-endpoint cases also exhibit distinct preferences when the data is normalized properly. Analysis of the local structural interactions which give rise to these preferences has not been studied here and is left for future work.

Multidimensional Residual Analysis of Point Process Models for Earthquake Occurrences

Residual analysis methods for examining the fit of multidimensional point process models are applied to point process models for the space–time–magnitude distribution of earthquake occurrences, using, in particular, the multidimensional version of Ogata's epidemic-type aftershock sequence (ETAS) model and a 30-year catalog of 580 earthquakes occurring in Bear Valley, California. One method involves rescaled residuals, obtained by transforming points along one coordinate to form a homogeneous Poisson process inside a random, irregular boundary. Another method involves thinning the point process according to the conditional intensity to form a homogeneous Poisson process on the original, untransformed space. The thinned residuals suggest that the fit of the model may be significantly improved by using an anisotropic spatial distance function in the estimation of the spatially varying background rate. Using rescaled residuals, it is shown that the temporal–magnitude distribution of aftershock activity is not separable, and that, in particular, in contrast to the ETAS model, the triggering density of earthquakes appears to depend on the magnitude of the secondary events in question. The residual analysis highlights that the fit of the space–time ETAS model may be improved by allowing the parameters governing the triggering density to vary for earthquakes of different magnitudes. Such modifications may be important because the ETAS model is widely used in seismology for hazard analysis.

Anaphor resolution as a function of spatial distance and priming: exploring the spatial distance effect in situation models.

Anaphor resolution has been found to depend on the spatial distance between the reader's focus of attention and the location of the anaphor referent in a spatially organized situation model (spatial distance effect; Rinck & Bower, 1995). This effect implies that a) the situation model is spatially organized and b) spatial distance has a stronger effect on the resolution of anaphoric reference than the text priming the anaphor referent. In three experiments, adult participants read 12 short narratives about protagonists moving around a building. Mentionning the location of the anaphor referent in text prior to the anaphoric sentence facilitated anaphor resolution. Decreased spatial distance consistently facilitated anaphor resolution, even when priming the anaphor referent affected anaphor resolution more strongly than spatial distance. Results are discussed with regard to the interpretation and reliability of the spatial distance effect and the interaction of different representational levels in the context of multi-level theories of text comprehension.