Spatial Ensemble Research Articles

Local artistic style formation in urban environment design

The article presents the corporate style as the leading project method in design in XXth century and its use in the design of the urban environment in the form of a local architectural and artistic style of the ensemble. It has become widespread in the organization of pedestrian streets in urban centers as a means of stylistic harmonization of diverse buildings and increasing the artistic expressiveness of the spatial ensemble. The striking example is the reconstruction of the Nikolayviertel in Berlin and his development into a territorial brand. At the same time, the graphic, artistic and stylistic accompaniment of the territorial brand became a logical continuation and development of its local architectural and artistic style, embedded in the architectural context, enhancing its imaginative component and the general artistic «positive». The modern post-industrial society brings with it changes that have affected the artistic and aesthetic qualities of the environment. Instead of an «open-air museum» with a clearly organized historical and architectural exposition, the visitor is offered a «settled» and a little shabby in the «shabby chic» style urban environment with picturesque green corners of cafes and restaurants.

NON-HYBRID ENSEMBLE SPATIAL REGRESSION ON HUMAN DEVELOPMENT INDEX (IPM) in CENTRAL JAVA

The human development index (HDI) is a measure to see an increase in regional development that has a very broad dimension, because it increases the quality of the population of an area in terms of life expectancy, education, and decent standard of living. In 2010 the Central Java HDI increased by 66.08% and increased by 4.44%, with the total HDI in 2017 of 70.52 percent. Spatial regression is the development of classical linear regression involving the region model. Spatial regression ensemble is a technique to be sent spasi spatial regression models by adding noise (additive noise). The type of spatial weighting used is Queen Contiguity. The selection of the best model using AIC and RMSE values. The purpose of this study is to provide an assessment of the distribution of HDI data in the Province of Central Java in 2017 and to do modeling using non-hybrid spatial ensemble regression regression. The results of this study are the SAR spatial method with ensemble giving results with AIC value of 143 and RMSE value of 1.3899 with a value of 90.09%. Significant variables on HDI are population density (X1), poverty (X2), school participation rates (X5), and average per capita per month for food and non-food (X7).

China meets Jeju Island: provincializing geopolitical economy in East Asia

ABSTRACT This article presents Jeju Island, the largest island in the Korean Peninsula, as a spatial ensemble newly constructed by various geopolitical and geoeconomic forces in East Asia. A new urban model called the Jeju Free International City has integrated China’s growing presence as a geoeconomic opportunity for local economic development. Yet, the subsequent establishment of Jeju Naval Base is premised on the vision of China as a new geopolitical threat that should be addressed. This incoherence results from the problematic articulation between the geopolitical–economic imaginations of the central government and economic development imperatives of the local growth coalition. This paper draws upon recent discussions of geopolitical economy and calls for provincializing geopolitical–economic analysis. The operation of zoning technologies in Jeju Island has been closely associated with the promotion of the contradictory visions of China’s rise, and as a result fails to reconcile geoeconomic and geopolitical discourses, practices and desires.

Spatial Ensembles of Copper-Silica with Carbon Nanotubes as Ultrastable Nanostructured Catalysts for Selective Hydrogenation.

Catalyst deactivation is one of the most important issues in heterogeneous catalysis. Constructing a stable nanoscale structure that maintains efficient activity and prolonged stability under redox conditions for catalysis, particularly hydrogenation reactions, remains attractive albeit the flourishing nanoscience. This work presents a facile route to synthesize a semi-encapsulated transition metal by assembling three-dimensional transition metal silicate nanotubes onto carbon nanotubes (CNTs) as precursors. The obtained materials expose an active surface of the transition metal for efficient catalysis and form a specific structure to inhibit the migration of metal nanoparticles (NPs) by establishing strong metal-support interactions. Cu@SiO2 prepared by common precipitation shows an inferior activity, and its performance is easily attenuated because of the aggregation of Cu NPs. The addition of CNTs as a carrier doubles the intrinsic activity of Cu catalysts. This hybrid catalyst, which consists of Cu species, SiO2, and CNTs, is among the best catalysts for dimethyl oxalate hydrogenation with boosting activity of 25 h-1 and enhanced stability of more than 200 h.

A priori evaluation of the Double-conditioned Conditional Source-term Estimation model for high-pressure heptane turbulent combustion using DNS data obtained with one-step chemistry

An evaluation of the submodels constituting the Double-Conditioned Source-term Estimation (DCSE) method for utilization in Large Eddy Simulation (LES) is presented which makes use of a single realization of a turbulent high-pressure reactive-flow database obtained from Direct Numerical Simulation (DNS). A filtered and coarsened DNS (FCDNS) field is first created to mimic a real LES field, and the FCDNS values for thermodynamic variables (temperature, density and species mass fractions) are used as inputs to the DCSE model to predict the FCDNS reaction rates. It is found that there are significant errors in the predictions of the filtered reaction rate compared to the template. These errors are attributed to three important aspects of the model. First, the conditional-filtered values of the thermodynamic variables are found by an inversion of an integral equation, but this inversion requires the definition of spatial ensembles in which it must be performed; these ensembles are a subset of the entire spatial domain containing the LES volumes. A drastic reduction in the number of ensembles from the number of LES volumes (i.e. the largest possible number of such ensembles) deteriorates the model, whereas a further and smaller such reduction has a small effect. Second, the procedure for the integral inversion introduces problems related to the method selected to regularize the inversion. Third, by far the largest source of error was found to be due to the modelling of the joint probability density function of the conditioning variables: assuming a β-PDF for the marginal PDF of the reaction progress variable is shown to be a particularly poor choice, as is the assumption that the conditioning variables are statistically independent.

Spatial ensemble prediction of hourly PM2.5 concentrations around Beijing railway station in China

Air pollution has a serious negative impact on human health and economic development. Railway stations have the maximum flow of people in a city. It is necessary to provide precise air pollution forecasting for railway station areas. In this study, a spatial ensemble model is proposed to predict hourly PM2.5 concentrations for the Beijing railway station. In the proposed model, the spatial analysis is realized by a spatial feature selection and a spatial ensemble. The spatial feature selection method can recognize the correlated monitoring sites around the Beijing railway station and rank them. An optimization method is utilized to optimize the weighting coefficients of different correlated sites. Base predictors of different correlated sites are integrated according to the weighting coefficients. Besides, a data decomposition method is also utilized to enhance the performance of the spatial model. In this study, data processing methods and spatial analysis methods are combined with each other to build the spatial ensemble model. Four quarters of PM2.5 concentration data are utilized to verify the effectiveness and stability of the proposed model. The proposed spatial ensemble model can outperform other comparison models.

Perception and decision mechanisms involved in average estimation of spatiotemporal ensembles

A number of studies on texture and ensemble perception have shown that humans can immediately estimate the average of spatially distributed visual information. The present study characterized mechanisms involved in estimating averages for information distributed over both space and time. Observers viewed a rapid sequence of texture patterns in which elements’ orientation were determined by dynamic Gaussian noise with variable spatial and temporal standard deviations (SDs). We found that discrimination thresholds increased beyond a certain spatial SD if temporal SD was small, but if temporal SD was large, thresholds remained nearly constant regardless of spatial SD. These data are at odds with predictions that threshold is uniquely determined by spatiotemporal SD. Moreover, a reverse correlation analysis revealed that observers judged the spatiotemporal average orientation largely depending on the spatial average orientation over the last few frames of the texture sequence – a recency effect widely observed in studies of perceptual decision making. Results are consistent with the notion that the visual system rapidly computes spatial ensembles and adaptively accumulates information over time to make a decision on spatiotemporal average. A simple computational model based on this notion successfully replicated observed data.

Global Ocean Extreme Wave Heights from Spatial Ensemble Data

Abstract A novel approach to estimation of extreme value ocean significant wave height is investigated, in which data from adjacent regions are pooled to form a spatial ensemble. The equivalent duration of this ensemble region is the sum of the durations of the data pooled to form the ensemble. To create such a spatial ensemble, data from regions to be pooled must be independent and identically distributed. ERA-Interim reanalysis data are used to investigate the requirement of independent and identically distributed data on a global basis. As a result, typical spatial ensembles are defined for a number of regions of the world and the 100-yr return period significant wave height is calculated for these regions. It is shown that the method can result in a reduction in the confidence interval for such extreme value estimates of between 30% and 60%. The method is demonstrated both with ERA-Interim data and altimeter data.

Spatial Ensemble Learning for Heterogeneous Geographic Data with Class Ambiguity

Class ambiguity refers to the phenomenon whereby similar features correspond to different classes at different locations. Given heterogeneous geographic data with class ambiguity, the spatial ensemble learning (SEL) problem aims to find a decomposition of the geographic area into disjoint zones such that class ambiguity is minimized and a local classifier can be learned in each zone. The problem is important for applications such as land cover mapping from heterogeneous earth observation data with spectral confusion. However, the problem is challenging due to its high computational cost. Related work in ensemble learning either assumes an identical sample distribution (e.g., bagging, boosting, random forest) or decomposes multi-modular input data in the feature vector space (e.g., mixture of experts, multimodal ensemble) and thus cannot effectively minimize class ambiguity. In contrast, we propose a spatial ensemble framework that explicitly partitions input data in geographic space. Our approach first preprocesses data into homogeneous spatial patches and uses a greedy heuristic to allocate pairs of patches with high class ambiguity into different zones. We further extend our spatial ensemble learning framework with spatial dependency between nearby zones based on the spatial autocorrelation effect. Both theoretical analysis and experimental evaluations on two real world wetland mapping datasets show the feasibility of the proposed approach.

A Spatial Ensemble Model for Rockfall Source Identification From High Resolution LiDAR Data and GIS

Rockfall source identification is the most challenging task in rockfall hazard and risk assessment. This difficulty rises in the areas where there is a presence of other types of the landslide, such as shallow landslide and debris flow. The aim of this paper is to develop and test a hybrid model that can accurately identify the source areas. High-resolution light detection and ranging data (LiDAR) was employed to derive the digital terrain model (DTM), from which several conditioning factors were extracted. These conditioning factors were optimized utilizing the ant colony optimization (ACO). Different machine learning algorithms, namely, logistic regression (LR), random tree (RT), random forest (RF), support vector machine (SVM), and artificial neural network (ANN), in addition to their ensemble models (stacking, bagging, and voting), were examined. This is based on the selected best subset of conditioning factors and inventory dataset. Stacking LR-RT (the best fit model) was then utilized to produce the probabilities of different landslide types. On the other hand, the Gaussian mixture model (GMM) was optimized and applied for automatically identifying the slope threshold of the occurrence of the different landslide types. In order to reduce the model sensitivity to the alteration in various conditioning factors and to improve the model computations performance, land use probability area was formed. The rockfall sources were identified by integrating the probability maps and the reclassified slope raster based on the GMM results. The accuracy assessment reveals that the developed hybrid model can identify the probable rockfall regions with an accuracy of 0.95 based on the validation dataset and 0.94 on based the training dataset. The slope thresholds calculated by GMM were found to be > 58°, 22°–58°, and 9°–22° for rockfall, shallow landslide, and debris flow, respectively. This indicates that the model can be generalized and replicated in different regions, and the proposed method can be applied in various landslides studies.

Deep learning approach for Fourier ptychography microscopy.

Convolutional neural networks (CNNs) have gained tremendous success in solving complex inverse problems. The aim of this work is to develop a novel CNN framework to reconstruct video sequences of dynamic live cells captured using a computational microscopy technique, Fourier ptychographic microscopy (FPM). The unique feature of the FPM is its capability to reconstruct images with both wide field-of-view (FOV) and high resolution, i.e. a large space-bandwidth-product (SBP), by taking a series of low resolution intensity images. For live cell imaging, a single FPM frame contains thousands of cell samples with different morphological features. Our idea is to fully exploit the statistical information provided by these large spatial ensembles so as to make predictions in a sequential measurement, without using any additional temporal dataset. Specifically, we show that it is possible to reconstruct high-SBP dynamic cell videos by a CNN trained only on the first FPM dataset captured at the beginning of a time-series experiment. Our CNN approach reconstructs a 12800×10800 pixel phase image using only ∼25 seconds, a 50× speedup compared to the model-based FPM algorithm. In addition, the CNN further reduces the required number of images in each time frame by ∼ 6×. Overall, this significantly improves the imaging throughput by reducing both the acquisition and computational times. The proposed CNN is based on the conditional generative adversarial network (cGAN) framework. We further propose a mixed loss function that combines the standard image domain loss and a weighted Fourier domain loss, which leads to improved reconstruction of the high frequency information. Additionally, we also exploit transfer learning so that our pre-trained CNN can be further optimized to image other cell types. Our technique demonstrates a promising deep learning approach to continuously monitor large live-cell populations over an extended time and gather useful spatial and temporal information with sub-cellular resolution.

A spatial ensemble approach for broad-area mapping of land surface properties

Understanding rapid global change requires land cover maps with broad spatial extent, but also fine spatial and temporal resolution. Developing such maps presents a unique challenge, as variability in relationships between spectral characteristics (i.e., predictors) and a response variable is likely to increase with the size of the region across which a model is built and applied. Although most mapping approaches apply the same predictor-response relationships globally across the entire modeling region, learned relationships from one local area may be invalid for another when predicting across broad extents. Here, we adapted a spatial ensemble approach borrowed from species distribution modeling to land cover mapping, and evaluated whether the approach could faithfully represent spatial variation in relationships between land cover and spectral data. The spatiotemporal exploratory model (STEM) uses an ensemble of regression trees defined within spatially overlapping support sets, producing a broad-extent map that reflects variability at the spatial scale of each constituent support set. As test cases for reference maps, we used 30-m-resolution forest canopy and impervious surface cover layers from the 2001 U.S. National Land Cover Database (NLCD) for the states of Washington, Oregon, and California. When testing strategies for support set size and sampling intensity, we found that predictor-response relationships were strongest when individual components of the spatial ensemble were small and when sampling intensity was high. Compared to aspatial bagged decision tree and random forest models, we found that the STEM approach successfully captured variation in our source maps, both globally and at scales smaller than the modeling region. Leveraging the spatial structure of a STEM, we also mapped per-pixel spatial variation in prediction confidence and the importance of different predictor variables. After testing appropriate spatial ensemble and sampling strategies, we extended the predictor-response relationships gleaned from the 2001 source maps into a yearly time series based on temporally-smoothed spectral data from the LandTrendr algorithm. The end products were yearly forest canopy and impervious surface cover time series representing 1990–2012. Formal evaluation showed that our temporally extended maps also closely resembled NLCD maps from 2011. The aim of this research was to cultivate the implicit relationships between spectral data and a given map, not improve them, but as the need for time series maps produced at both broad extents and fine resolutions increases, our results demonstrate that an ensemble of locally defined estimators is potentially more appropriate than conventional ensemble models for land cover mapping across broad extents.

PRECONDITIONS OF CHANGES IN THE ARCHITECTURAL AND URBAN BUILDING COMPONENT OF THE SOCIO-CULTURAL ENVIRONMENT OF THE CITY

Up-to-date studies carried out by sociologists, psychologists, political scientists, cultural scientists and other theorists who study society as a social system are often based on the issue on the revolutionary changes introduced by the latest high-end technologies into the processes of communication between people. Communicative, technological communication fundamentally changes the understanding of many processes and types of sociocultural relations. In the future, these changes will fundamentally alter the approach to the formation of the urban environment in general and communication spaces in particular. This paper presents an attempt to consider the impact of virtual communication on the city. The positive and negative aspects of these changes are considered and, ultimately, an attempt on the formation of a spatial communication model in the modern city is made. The options for a comprehensive assessment of the social quality of the urban environment are described. The authors’ classification of the communication spaces of the modern city became a foundation for a study aimed to identify potential locations of these environmental objects and systems. The hypothesis of a phased creation of hybrid spatial ensembles within the architectural units constituting the environment for various types of interpersonal communication, in the form of a synthesis of media technologies and urban planning is generated.

Global ensemble texture representations are critical to rapid scene perception.

Traditionally, recognizing the objects within a scene has been treated as a prerequisite to recognizing the scene itself. However, research now suggests that the ability to rapidly recognize visual scenes could be supported by global properties of the scene itself rather than the objects within the scene. Here, we argue for a particular instantiation of this view: That scenes are recognized by treating them as a global texture and processing the pattern of orientations and spatial frequencies across different areas of the scene without recognizing any objects. To test this model, we asked whether there is a link between how proficient individuals are at rapid scene perception and how proficiently they represent simple spatial patterns of orientation information (global ensemble texture). We find a significant and selective correlation between these tasks, suggesting a link between scene perception and spatial ensemble tasks but not nonspatial summary statistics In a second and third experiment, we additionally show that global ensemble texture information is not only associated with scene recognition, but that preserving only global ensemble texture information from scenes is sufficient to support rapid scene perception; however, preserving the same information is not sufficient for object recognition. Thus, global ensemble texture alone is sufficient to allow activation of scene representations but not object representations. Together, these results provide evidence for a view of scene recognition based on global ensemble texture rather than a view based purely on objects or on nonspatially localized global properties. (PsycINFO Database Record

A Study on Regularized Common Spatial Pattern Ensemble Method Based on Filter Bank for Small Sample Setting Motor Imagery EEG

A Study on Regularized Common Spatial Pattern Ensemble Method Based on Filter Bank for Small Sample Setting Motor Imagery EEG

Lidar Method to Estimate Emission Rates from Extended Sources

AbstractPollutant emissions to the atmosphere commonly derive from nonpoint sources that are extended in space. Such sources may contain area, volume, line, or a combination of emission types. Currently, point measurements, often combined with models, are the primary means by which atmospheric emission rates are estimated from extended sources. Point measurement arrays often lack in spatial and temporal resolution and accuracy. In recent years, lidar has supplemented point measurements in agricultural research by sampling spatial ensembles nearly instantaneously. Here, a methodology using backscatter data from an elastic scanning lidar is presented to estimate emission rates from extended sources. To demonstrate the approach, a known amount of particulate matter was released upwind of a vegetative environmental buffer, a barrier designed to intercept emissions from animal production facilities. The emission rate was estimated downwind of the buffer, and the buffer capture efficiency (percentage of particles captured) was calculated. Efficiencies ranged from 21% to 74% and agree with the ranges previously published. A comprehensive uncertainty analysis of the lidar methodology was performed, revealing an uncertainty of 20% in the emission rate estimate; suggestions for significantly reducing this uncertainty in future studies are made. The methodology introduced here is demonstrated by estimating the efficiency of a vegetative buffer, but it can also be applied to any extended emission source for which point samples are inadequate, such as roads, animal feedlots, and cotton gin operations. It can also be applied to any pollutant for which a lidar system is configured, such as particulate matter, carbon dioxide, and ammonia.

Exploring Eddy-Covariance Measurements Using a Spatial Approach: The Eddy Matrix

Taylor’s frozen turbulence hypothesis states that “standard” eddy-covariance measurements of fluxes at a fixed location can replace a spatial ensemble of instantaneous values at multiple locations. For testing this hypothesis, a unique turbulence measurement set-up was used for two measurement campaigns over desert (Namibia) and grassland (Germany) in 2012. This “Eddy Matrix” combined nine ultrasonic anemometer–thermometers and 17 thermocouples in a 10 m $$\times $$ 10 m regular grid with 2.5-m grid distance. The instantaneous buoyancy flux derived from the spatial eddy covariance of the Eddy Matrix was highly variable in time (from $$-0.3$$ to 1 m K s $$^{-1}$$ ). However, the 10-min average reflected 83 % of the reference eddy-covariance flux with a good correlation. By introducing a combined eddy-covariance method (the spatial eddy covariance plus the additional flux of the temporal eddy covariance of the spatial mean values), the mean flux increases by 9 % relative to the eddy-covariance reference. Considering the typical underestimation of fluxes by the standard eddy-covariance method, this is seen as an improvement. Within the limits of the Eddy Matrix, Taylor’s hypothesis is supported by the results.

No evidence for a fixed object limit in working memory: Spatial ensemble representations inflate estimates of working memory capacity for complex objects.

A central question for models of visual working memory is whether the number of objects people can remember depends on object complexity. Some influential "slot" models of working memory capacity suggest that people always represent 3-4 objects and that only the fidelity with which these objects are represented is affected by object complexity. The primary evidence supporting this claim is the finding that people can detect large changes to complex objects (consistent with remembering at least 4 individual objects), but that small changes cannot be detected (consistent with low-resolution representations). Here we show that change detection with large changes greatly overestimates individual item capacity when people can use global representations of the display to detect such changes. When the ability to use such global ensemble or texture representations is reduced, people remember individual information about only 1-2 complex objects. This finding challenges models that propose people always remember a fixed number of objects, regardless of complexity, and supports a more flexible model with an important role for spatial ensemble representations.

An efficient semi-supervised classification approach for hyperspectral imagery

In this paper, an efficient semi-supervised support vector machine (SVM) with segmentation-based ensemble (S2SVMSE) algorithm is proposed for hyperspectral image classification. The algorithm utilizes spatial information extracted by a segmentation algorithm for unlabeled sample selection. The unlabeled samples that are the most similar to the labeled ones are found and the candidate set of unlabeled samples to be chosen is enlarged to the corresponding image segments. To ensure the finally selected unlabeled samples be spatially widely distributed and less correlated, random selection is conducted with the flexibility of the number of unlabeled samples actually participating in semi-supervised learning. Classification is also refined through a spectral–spatial feature ensemble technique. The proposed method with very limited labeled training samples is evaluated via experiments with two real hyperspectral images, where it outperforms the fully supervised SVM and the semi-supervised version without spectral–spatial ensemble.

On macroscopic quantum phenomena in biomolecules and cells: from Levinthal to Hopfield.

In the context of the macroscopic quantum phenomena of the second kind, we hereby seek for a solution-in-principle of the long standing problem of the polymer folding, which was considered by Levinthal as (semi)classically intractable. To illuminate it, we applied quantum-chemical and quantum decoherence approaches to conformational transitions. Our analyses imply the existence of novel macroscopic quantum biomolecular phenomena, with biomolecular chain folding in an open environment considered as a subtle interplay between energy and conformation eigenstates of this biomolecule, governed by quantum-chemical and quantum decoherence laws. On the other hand, within an open biological cell, a system of all identical (noninteracting and dynamically noncoupled) biomolecular proteins might be considered as corresponding spatial quantum ensemble of these identical biomolecular processors, providing spatially distributed quantum solution to a single corresponding biomolecular chain folding, whose density of conformational states might be represented as Hopfield-like quantum-holographic associative neural network too (providing an equivalent global quantum-informational alternative to standard molecular-biology local biochemical approach in biomolecules and cells and higher hierarchical levels of organism, as well).