Spatio-temporal Regularization Research Articles

Sequence learning is surprisingly fragile in visual search.

Extensive research has shown that people are sensitive to statistical regularities of visual stimuli, such as a repeated sequence of object locations. Such learning, however, has primarily occurred for objects presented in isolation. Here, we tested whether sequence learning also manifested in complex displays. Using variants of the serial reaction time task, we asked participants to report the screen quadrant of a letter T, whose location followed a 12-trial sequence that repeated 30 times over 360 trials. In different experiments, we manipulated the nature of distractors surrounding the target. The T could appear in isolation, as a color singleton among distractors with fixed or variable locations, or as a conjunction search target. Sequence learning-expressed as elevated response time when the learned sequence was disrupted-decreased as spatial noise increased. Learning was robust when the T appeared in isolation or when it was surrounded by distractors that did not change locations. It was reduced in feature search and eliminated in conjunction search. These findings suggest that target locations are coded in relation to concurrently presented distractors. Variability in distractor locations disrupts target location sequence learning, revealing a limit to people's ability to extract and use spatiotemporal regularities in complex environments. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Sequence learning is surprisingly fragile in visual search

Extensive research has shown that people are sensitive to statistical regularities of visual stimuli, such as a repeated sequence of object locations. Such learning, however, has primarily occurred for objects presented in isolation. Here, we tested whether sequence learning also manifested in complex displays. Using variants of the serial reaction time task, we asked participants to report the screen quadrant of a letter T, whose location followed a 12-trial sequence that repeated 30 times over 360 trials. In different experiments, we manipulated the nature of distractors surrounding the target. The T could appear in isolation, as a color singleton among distractors with fixed or variable locations, or as a conjunction search target. Sequence learning-expressed as elevated response time when the learned sequence was disrupted-decreased as spatial noise increased. Learning was robust when the T appeared in isolation or when it was surrounded by distractors that did not change locations. It was reduced in feature search and eliminated in conjunction search. These findings suggest that target locations are coded in relation to concurrently presented distractors. Variability in distractor locations disrupts target location sequence learning, revealing a limit to people's ability to extract and use spatiotemporal regularities in complex environments. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

T-product factorization method for internet traffic data completion with spatio-temporal regularization

T-product factorization method for internet traffic data completion with spatio-temporal regularization

Learning optical flow for fast MRI reconstruction

Reconstructing high-quality magnetic resonance images (MRIs) from undersampled raw data is of great interest from both technical and clinical point of views. To this date, however, it is still a mathematically and computationally challenging problem due to its severe ill-posedness, resulting from the highly undersampled data. Whilst a number of techniques have been presented to improve image reconstruction, they only account for spatio-temporal regularisation, which shows its limitations in several relevant scenarios including dynamic data. In this work, we propose a new mathematical model for the reconstruction of high-quality medical MRI from few measurements. Our proposed approach combines—in a multi-task and hybrid model—the traditional compressed sensing formulation for the reconstruction of dynamic MRI with motion compensation by learning an optical flow approximation. More precisely, we propose to encode the dynamics in the form of an optical flow model that is sparsely represented over a learned dictionary. This has the advantage that ground truth data is not required in the training of the optical flow term. Furthermore, we present an efficient optimisation scheme to tackle the non-convex problem based on an alternating splitting method. We demonstrate the potentials of our approach through an extensive set of numerical results using different datasets and acceleration factors. Our combined approach reaches and outperforms several state-of-the-art techniques for multi-tasking reconstruction and other classic variational reconstruction schemes. Finally, we show the ability of our technique to transfer phantom based knowledge to real datasets.

Joint spatiotemporal regularization and scale-aware correlation filters for visual tracking

Correlation filters (CF)-based tracking methods have drawn extensive attention due to their impressive performance in visual object tracking (VOT) challenges. However, the existing CF-based trackers incur the inherent problems of boundary effect and filter degradation. Meanwhile, the scale variants of targets result in tracking-drift problems, which degrade the tracking accuracy significantly. To address the aforementioned problems, a tracking model named spatiotemporal regularization and scale-aware correlation filters (STR-SACF) is proposed. The STR-SACF model consists of two CFs, namely translation filter and scale filter (SF). The translation filter improves the accuracy of target localization, whereas the SF achieves the optimal and fast scale estimation. The proposed tracking model can improve the tracking accuracy while reducing computational complexity. Moreover, a spatiotemporal regularization term is introduced into the translation filter and SF to suppress boundary effect and filter degradation. The proposed model is effectively optimized based on the alternating direction method of multipliers algorithm. Experimental results on four challenging tracking benchmarks, i.e., OTB2013, OTB2015, TC128, and VOT2016, demonstrate the superiority of the proposed tracker compared with more than 20 state-of-the-art trackers.

Value chain organization models of Taiwanese electronic information enterprises in Mainland China and their spatiotemporal evolution processes.

This study examines Taiwanese investment in Mainland China as it is an important part of cross-strait economic cooperation. Using sample data from Taiwanese-listed electronic information enterprises in Mainland China (1990–2016), this study combines ArcGIS spatial visualization and case analysis to investigate their value chain organization models and spatiotemporal evolution regularity. The results show that the value chain of the electronic information industry for Taiwanese investments in Mainland China has three models: vertical integration, modularization in production sharing, and production extension. Vertical integration is the main production organization model of these Taiwanese listed electronic information enterprises, expanding from single production to the entire manufacturing value chain, followed by sales, and finally R&D. This model is still in use in the Western Taiwan Straits Economic Zone, whereas the other four Taiwanese investment agglomerations, namely the Bohai Economic Rim, Yangtze River Delta, Pearl River Delta, and Western Delta Economic Circle, began to expand to both ends of the production link, particularly to the sales link. High-value -added enterprises adopting production sharing models began to show a trend of expansion to inland cities, and enterprises adopting the manufacturing–sales model (a production expansion model) had the widest distribution. Finally, at the city level, the value chain fragmentation structure of Taiwanese and developed countries’ cross-border (multinational) enterprises in Mainland China were consistent, that is, they matched the Chinese city hierarchy; at the regional level, however, the Western Delta Economic Circle pioneered to become a hub for Taiwanese electronic to information enterprises set up their R&D and sales links in Mainland China. Investigating chain-alike spatiotemporal expansion of Taiwanese investment in Mainland China is important for the integration and development of the value chain, production network, and enterprise spatial organization theories.

Features of generation, migration and accumulation of hydrocarbons in the eastern part of the Skythian plate

The article shows the main spatio-temporal regularities of the development of the processes of generation, migration and accumulation of hydrocarbon fluids in the eastern part of the Scythian plate. The authors studied the features of the generationaccumulation hydrocarbon systems of the northern platform side - the Karpinsko-Mangyshlak and East Ciscaucasian oil and gas regions based on the application of technologies of basin analysis and modeling of hydrocarbon systems. The research results indicate that the following hydrocarbon systems are distinguished in the sedimentary cover of the eastern part of the Scythian plate: Jurassic, Cretaceous, Paleocene-Eocene, and Oligocene-Miocene systems. Based on the results of the studies and modeling, the main directions of further prospecting and exploration work in the eastern part of the Scythian plate were determined. The study area was differentiated by the nature of the predicted oil and gas content, the age of the promising complexes and the confinement to tectonic structures and zones. Keywords: generation; migration; accumulation; hydrocarbons; Scythian plate; hydrocarbon systems; basin analysis; modeling; source rocks.

Abnormal spatiotemporal expression pattern of progranulin and neurodevelopment impairment in VPA-induced ASD rat model

Some environmental risk factors have been proven to contribute to the etiology of autism spectrum disorder (ASD). Exposure to the antiepileptic drug valproic acid (VPA) during pregnancy significantly increases the risk of ASD in humans, and consequently is utilized as a validated animal model of ASD in rodents; however, the precise molecular and cellular mechanisms remain ill-defined. In the present study, we investigated the effect of prenatal VPA exposure on the spatiotemporal dynamics of Progranulin (PGRN) expression, neuronal apoptosis, synapse density, and AKT/GSK-3β pathway activation in the brains of VPA-exposed offspring. Results from behavioral tests were consistent with prior studies showing impaired sociability, restricted interests and increased repetitive behaviors in VPA rats at postnatal days 28–32. Our data also indicated that VPA exposure resulted in abnormal dynamics of PGRN expression in different brain regions at the different development stages. The temporal and spatial patterns of PGRN expression were consistent with the spatiotemporal regularity of abnormalities, which observed in apoptosis-related protein levels, neuron numbers, dendritic spine density, synapse-related protein levels, and AKT/GSK-3β phosphorylation in VPA rats. It suggests that prenatal VPA exposure may affect the spatiotemporal regularity of neuronal apoptosis and synaptic development/regression via interfering with the spatiotemporal process of PGRN expression and downstream AKT/GSK-3β pathway activation. This may be a potential mechanism of the abnormal neuroanatomical changes and ASD-like behaviors in VPA-induced ASD.

Regime-related regularities in river flow revealed by Aksentijevic-Gibson complexity

A streamflow time series contains a large quantity of information and finding the instruments that are capable of accessing this information remains an important task. Using the Kolmogorov complexity (KC) and its derivatives, it has previously been shown that the degree of randomness in streamflow partly depends on its orographic characteristics. This paper applied a change-based complexity measure (Aksentijevic-Gibson complexity – AG) to investigate the spatiotemporal regularities of monthly streamflow of seven rivers from Bosnia and Herzegovina for a period of 1965–1986, and evaluate their hydrological effects. A unique advantage of this complexity measure is its ability to quantify large-scale complexity and to zoom in on low-level structure and detect patterns that are normally inaccessible. Ten time series created from the seven rivers were examined and temporal and structural complexity profiles of different streamflows and annual periodicities associated with bimodal/mixed flow regimes were obtained. The AG complexity can be employed as a standard instrument for the analysis of hydrological data and it holds the promise of uncovering patterns and rhythms in the data that cannot be captured by KC and other complexity measures.

Shrub decline and expansion of wetland vegetation revealed by very high resolution land cover change detection in the Siberian lowland tundra

Vegetation change, permafrost degradation and their interactions affect greenhouse gas fluxes, hydrology and surface energy balance in Arctic ecosystems. The Arctic shows an overall “greening” trend (i.e. increased plant biomass and productivity) attributed to expansion of shrub vegetation. However, Arctic shrub dynamics show strong spatial variability and locally “browning” may be observed. Mechanistic understanding of greening and browning trends is necessary to accurately assess the response of Arctic vegetation to a changing climate. In this context, the Siberian Arctic is an understudied region. Between 2010 and 2019, increased browning (as derived from the MODIS Enhanced Vegetation Index) was observed in the Eastern Siberian Indigirka Lowlands. To support interpretation of local greening and browning dynamics, we quantified changes in land cover and transition probabilities in a representative tundra site in the Indigirka Lowlands using a timeseries of three very high resolution (VHR) (0.5 m) satellite images acquired between 2010 and 2019. Using spatiotemporal Potts model regularization, we substantially reduced classification errors related to optical and phenological inconsistencies in the image material. VHR images show that recent browning was associated with declines in shrub, lichen and tussock vegetation and increases in open water, sedge and especially Sphagnum vegetation. Observed formation and expansion of small open water bodies in shrub dominated vegetation suggests abrupt thaw of ice-rich permafrost. Transitions from open water to sedge and Sphagnum, indicate aquatic succession upon disturbance. The overall shift towards open water and wetland vegetation suggests a wetting trend, likely associated with permafrost degradation. Landsat data confirmed widespread expansion of surface water throughout the Indigirka Lowlands. However, the increase in the area of small water bodies observed in VHR data was not visible in Landsat-derived surface water data, which suggests that VHR data is essential for early detection of small-scale disturbances and associated vegetation change in permafrost ecosystems.

Detecting spatiotemporal extents of traffic congestion: a density-based moving object clustering approach

ABSTRACT Traffic congestion detection poses challenges in spatiotemporal data mining and intelligent transportation research. Existing studies primarily detect traffic congestion based on the speed estimation of traffic flows. Such detection techniques may overlook the formation of traffic congestion in space and time. This research proposes a density-based approach to moving object clustering that extracts the spatiotemporal extents of traffic congestion in three steps. The first step applies a map-matching strategy to project original trajectory points in a planar space onto a road network space and segments the trajectories into consecutive time windows. In the second step, we statistically detect moving clusters with significantly high-density subject to network constrained clustering. The final third step determines moving clusters indicative of traffic congestion through the analysis of both vehicle speed and time spans. Comparative experiments on both simulated trajectories and the real-life taxi trajectories in Wuchang demonstrate that the proposed method outperforms other methods through quantitative evaluations using three indicators, i.e. the precision, recall and F1 value. The proposed approach can illustrate the spatiotemporal regularities of traffic congestion, which can inform dynamic route planning and network design optimization.

Occipital sleep spindles predict sequence learning in a visuo-motor task.

Study ObjectivesThe brain appears to use internal models to successfully interact with its environment via active predictions of future events. Both internal models and the predictions derived from them are based on previous experience. However, it remains unclear how previously encoded information is maintained to support this function, especially in the visual domain. In the present study, we hypothesized that sleep consolidates newly encoded spatio-temporal regularities to improve predictions afterwards.MethodsWe tested this hypothesis using a novel sequence-learning paradigm that aimed to dissociate perceptual from motor learning. We recorded behavioral performance and high-density electroencephalography (EEG) in male human participants during initial training and during testing two days later, following an experimental night of sleep (n = 16, including high-density EEG recordings) or wakefulness (n = 17).ResultsOur results show sleep-dependent behavioral improvements correlated with sleep-spindle activity specifically over occipital cortices. Moreover, event-related potential (ERP) responses indicate a shift of attention away from predictable to unpredictable sequences after sleep, consistent with enhanced automaticity in the processing of predictable sequences.ConclusionsThese findings suggest a sleep-dependent improvement in the prediction of visual sequences, likely related to visual cortex reactivation during sleep spindles. Considering that controls in our experiments did not fully exclude oculomotor contributions, future studies will need to address the extent to which these effects depend on purely perceptual versus oculomotor sequence learning.

Concerning the issue of resource assessment for solar energy utilization systems in the climatic conditions of the Russian Federation

The article deals with the distribution of the energy potential of solar radiation in the territory of the Russian Federation. The spatio-temporal regularities are revealed, which make it possible to assess the resource provision of engineering systems in a given renewable energy source. Graphical distributions of solar radiation for the northern latitudes of the Russian Federation, including the energy irradiance of vertical orientation surfaces by cardinal directions, are constructed. Equations are obtained for the possibilities of solar radiation depending on the considered month and latitude of the area. An amendment has been introduced that takes into account the decrease in the radiation intensity with average cloud cover for the regions. Revealed the territorial-temporal variability of the possibility of solar radiation, which makes it possible to assess the prospects of using or another system of utilization of radiation.

Evolution of Hydrocarbon Systems Within the Eastern Part of the Scythian Plate

On the basis of the performed 2D modeling, the authors established the main spatiotemporal regularities of the development of the processes of generation, migration and accumulation of hydrocarbon fluids in the platform part of the Eastern Ciscaucasia. The modeling results showed the saturation with hydrocarbons of the terrigenous strata of the Anisian and Landian stages, terrigenous sediments of the Middle Jurassic, Lower Cretaceous, as well as a single saturation of carbonates of the Upper Cretaceous and Lower Maikop, probably associated with the development of areas of abnormally high reservoir pressures. The degree of rock saturation is relatively high and reaches 80% for the sandy strata of the Middle Jurassic. For carbonate-terrigenous sediments of the Middle Triassic, saturation varies from 45-65%.

Deformable motion compensation for interventional cone-beam CT

Image-guided therapies in the abdomen and pelvis are often hindered by motion artifacts in cone-beam CT (CBCT) arising from complex, non-periodic, deformable organ motion during long scan times (5–30 s). We propose a deformable image-based motion compensation method to address these challenges and improve CBCT guidance. Motion compensation is achieved by selecting a set of small regions of interest in the uncompensated image to minimize a cost function consisting of an autofocus objective and spatiotemporal regularization penalties. Motion trajectories are estimated using an iterative optimization algorithm (CMA-ES) and used to interpolate a 4D spatiotemporal motion vector field. The motion-compensated image is reconstructed using a modified filtered backprojection approach. Being image-based, the method does not require additional input besides the raw CBCT projection data and system geometry that are used for image reconstruction. Experimental studies investigated: (1) various autofocus objective functions, analyzed using a digital phantom with a range of sinusoidal motion magnitude (4, 8, 12, 16, 20 mm); (2) spatiotemporal regularization, studied using a CT dataset from The Cancer Imaging Archive with deformable sinusoidal motion of variable magnitude (10, 15, 20, 25 mm); and (3) performance in complex anatomy, evaluated in cadavers undergoing simple and complex motion imaged on a CBCT-capable mobile C-arm system (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Gradient entropy was found to be the best autofocus objective for soft-tissue CBCT, increasing structural similarity (SSIM) by 42%–92% over the range of motion magnitudes investigated. The optimal temporal regularization strength was found to vary widely (0.5–5 mm−2) over the range of motion magnitudes investigated, whereas optimal spatial regularization strength was relatively constant (0.1). In cadaver studies, deformable motion compensation was shown to improve local SSIM by ∼17% for simple motion and ∼21% for complex motion and provided strong visual improvement of motion artifacts (reduction of blurring and streaks and improved visibility of soft-tissue edges). The studies demonstrate the robustness of deformable motion compensation to a range of motion magnitudes, frequencies, and other factors (e.g. truncation and scatter).

Spatiotemporal Synchronization of Diffusively Coupled Modified Logistic Map Under Complex Network

We study spatiotemporal synchronization under complex network of diffusively coupled chaotic modified logistic map. In modified logistic map a new parameter is introduced such that nonlinear term is in fractional power. The complex network is dynamic whose coupling connections change stochastically in time. Here we investigate the spatiotemporal dynamics of coupled modified logistic maps whose coupling connections are rewired randomly, and we determine (1) the effects of variation of newly induced parameter $$\beta $$ , (2) the effects of variation of low and high rewiring probability, (3) the effects of variation of growth rate r and (4) the effects of variation of different randomness and linear stability analysis of the synchronized steady-state solution. We have calculated analytically the critical coupling coefficient for the transition to spatiotemporal regularity of the lattice. The analytical results match well with the numerical simulation results. The variation of the basin size with respect to coupling strength and rewiring probability with various randomness and coupling is plotted. The simulation results do not change significantly with the variation of lattice sizes.

Spatiotemporal Bayesian Regularization for Cardiac Strain Imaging: Simulation and In Vivo Results.

Cardiac strain imaging (CSI) plays a critical role in the detection of myocardial motion abnormalities. Displacement estimation is an important processing step to ensure the accuracy and precision of derived strain tensors. In this paper, we propose and implement Spatiotemporal Bayesian regularization (STBR) algorithms for two-dimensional (2-D) normalized cross-correlation (NCC) based multi-level block matching along with incorporation into a Lagrangian cardiac strain estimation framework. Assuming smooth temporal variation over a short span of time, the proposed STBR algorithm performs displacement estimation using at least four consecutive ultrasound radio-frequency (RF) frames by iteratively regularizing 2-D NCC matrices using information from a local spatiotemporal neighborhood in a Bayesian sense. Two STBR schemes are proposed to construct Bayesian likelihood functions termed as Spatial then Temporal Bayesian (STBR-1) and simultaneous Spatiotemporal Bayesian (STBR-2). Radial and longitudinal strain estimated from a finite-element-analysis (FEA) model of realistic canine myocardial deformation were utilized to quantify strain bias, normalized strain error and total temporal relative error (TTR). Statistical analysis with one-way analysis of variance (ANOVA) showed that all Bayesian regularization methods significantly outperform NCC with lower bias and errors (p < 0.001). However, there was no significant difference among Bayesian methods. For example, mean longitudinal TTR for NCC, SBR, STBR-1 and STBR-2 were 25.41%, 9.27%, 10.38% and 10.13% respectively An in vivo feasibility study using RF data from ten healthy mice hearts were used to compare the elastographic signal-to-noise ratio (SNRe) calculated using stochastic analysis. STBR-2 had the highest expected SNRe both for radial and longitudinal strain. The mean expected SNRe values for accumulated radial strain for NCC, SBR, STBR-1 and STBR-2 were 5.03, 9.43, 9.42 and 10.58, respectively. Overall results suggest that STBR improves CSI in vivo.

Deep Dichromatic Model Estimation Under AC Light Sources.

The dichromatic reflection model has been popularly exploited for computer vison tasks, such as color constancy and highlight removal. However, dichromatic model estimation is an severely ill-posed problem. Thus, several assumptions have been commonly made to estimate the dichromatic model, such as white-light (highlight removal) and the existence of highlight regions (color constancy). In this paper, we propose a spatio-temporal deep network to estimate the dichromatic parameters under AC light sources. The minute illumination variations can be captured with high-speed camera. The proposed network is composed of two sub-network branches. From high-speed video frames, each branch generates chromaticity and coefficient matrices, which correspond to the dichromatic image model. These two separate branches are jointly learned by spatio-temporal regularization. As far as we know, this is the first work that aims to estimate all dichromatic parameters in computer vision. To validate the model estimation accuracy, it is applied to color constancy and highlight removal. Both experimental results show that the dichromatic model can be estimated accurately via the proposed deep network.

中国水资源-能源-粮食-支撑系统安全测度及协调发展

探索我国水资源-能源-粮食-支撑系统安全及时空演变规律，有利于促进中国水资源-能源-粮食-经济社会-管理维护的可持续发展。采用模糊物元法和耦合协调度模型，借助ArcGIS空间分析及核密度和基尼系数、变异系数，对我国31个省、市、自治区2004-2017年水资源-能源-粮食-经济社会-管理维护安全水平及耦合协调度进行测算及时空对比分析。结果表明：①时间上，我国安全测度指数总体呈上升趋势；空间上，我国安全测度水平除青海、西藏外普遍较好；②各子系统情况：经济社会子系统增长较快、水资源子系统水平较差、粮食安全指数变差、能源子系统分化现象显著、管理维护小幅度提升；③我国安全测度协调度2004年0.170提高至2017年0.348，总体呈上升趋势；④安全测度协调度的空间演化基本可以分为上升型、下降型、平滑型和波动型四类，以波动型居多。;In order to promote the sustainable development of water resources-energy-food-support system security in China, we explore security of water resources-energy-food and its spatio-temporal development regularity in China. In this paper, the fuzzy matter-element method and the coupling coordination degree model are used to calculate and analyze the water resources-energy-food ecological security level and coupling coordination degree of 31 provinces, cities and autonomous regions in China from 2004 to 2017 by using ArcGIS spatial analysis, core density, Gini coefficient and variation coefficient. The results showed that:① in terms of time, security index of China was on the rise; in terms of space, China's security index level was generally good except Qinghai and Tibet. ② The economic and social subsystem grew rapidly, the level of water resources subsystem and the index of food security were poor. The energy subsystem is differentiated significantly and small improvement in management and maintenance.③ The coordination degree of security in China increased from 0.170 in 2004 to 0.348 in 2017, showing an overall upward trend. ④ The spatial evolution of the coordination degree of security could be divided into four types:ascending type, descending type, smooth type, and undulating type.

Spatiotemporal distribution of heavy and extreme snowfalls in the Transcarpathian region

This paper presents a spatiotemporal analysis of the dynamics of heavy and extreme snowfalls in the Transcarpathian region during 1990-2019. Data on snowfalls are obtained from the observation points of the state hydrometeorological network of the Transcarpathian Regional Center of Hydrometeorology. Also there are data included from the Pozhezhevska snow avalanche station (Ivano-Frankivsk region) as a representative observation point for the highland zone of the eastern part of the Transcarpathian region. The analysis took into account the date of snowfall, the amount of precipitation that fell during the snowfall,l and its duration. The recurrence of different indicators of snowfall distribution was calculated for each observation point separately for the specified thirty-year period. Some results were mapped by the isolines using kriging interpolation. Spatio-temporal heterogeneities and regularities in the distribution of heavy and extreme snowfalls have been revealed.