Space-time Processing Research Articles

Self-reactivated rupture during the 2019 Mw = 8 northern Peru intraslab earthquake

The 2019/05/26 Northern Peru earthquake (Mw=8) is a major intermediate-depth earthquake that occurred close to the eastern edge of the Nazca slab flat area. We analyze its rupture process using high-frequency back-projection and seismo-geodetic broadband inversion. The latter approach shows that the earthquake propagated with almost purely normal faulting along the 60° eastward dipping plane. Both imaging techniques provide a very consistent image of the peculiar space-time rupture process of this earthquake: its 60-second long rupture is characterized both by a main northward propagation (resulting in a rupture extent of almost 200 km in this direction) and by a reactivation phase of the hypocentral area, particularly active 35 s to 50 s after origin time. Given the depth of this earthquake (125-140 km), the reactivation time window coincides with the arrival time of the surface-reflected elastic wavefield. Computed values of the dynamic Coulomb stresses associated with this wavefield are of the order of ten to several tens of kPa, in a range of values where dynamic triggering has already been observed. The reactivation phase of the Peru earthquake may thus originate from fault areas that were brought close to rupture by the initial rupture front before being triggered by stress increments provided by the reflected wavefield. Source time function complexity observed for other large intermediate-depth earthquakes further suggests that such a mechanism is not an isolated case.

Quantifying deviations from separability in space-time functional processes

The estimation of covariance operators of spatio-temporal data is in many applications only computationally feasible under simplifying assumptions, such as separability of the covariance into strictly temporal and spatial factors. Powerful tests for this assumption have been proposed in the literature. However, as real world systems, such as climate data are notoriously inseparable, validating this assumption by statistical tests, seems inherently questionable. In this paper we present an alternative approach: By virtue of separability measures, we quantify how strongly the data’s covariance operator diverges from a separable approximation. Confidence intervals localize these measures with statistical guarantees. This method provides users with a flexible tool, to weigh the computational gains of a separable model against the associated increase in bias. As separable approximations we consider the established methods of partial traces and partial products, and develop weak convergence principles for the corresponding estimators. Moreover, we also prove such results for estimators of optimal, separable approximations, which are arguably of most interest in applications. In particular we present for the first time statistical inference for this object, which has been confined to estimation previously. Besides confidence intervals, our results encompass tests for approximate separability. All methods proposed in this paper are free of nuisance parameters and do not require computationally expensive resampling procedures. A simulation study underlines the advantages of our approach and its applicability is demonstrated by the investigation of German annual temperature data.

Iterative Knowledge Exchange Between Deep Learning and Space-Time Spectral Clustering for Unsupervised Segmentation in Videos.

We propose a dual system for unsupervised object segmentation in video, which brings together two modules with complementary properties: a space-time graph that discovers objects in videos and a deep network that learns powerful object features. The system uses an iterative knowledge exchange policy. A novel spectral space-time clustering process on the graph produces unsupervised segmentation masks passed to the network as pseudo-labels. The net learns to segment in single frames what the graph discovers in video and passes back to the graph strong image-level features that improve its node-level features in the next iteration. Knowledge is exchanged for several cycles until convergence. The graph has one node per each video pixel, but the object discovery is fast. It uses a novel power iteration algorithm computing the main space-time cluster as the principal eigenvector of a special Feature-Motion matrix without actually computing the matrix. The thorough experimental analysis validates our theoretical claims and proves the effectiveness of the cyclical knowledge exchange. We also perform experiments on the supervised scenario, incorporating features pretrained with human supervision. We achieve state-of-the-art level on unsupervised and supervised scenarios on four challenging datasets: DAVIS, SegTrack, YouTube-Objects, and DAVSOD. We will make our code publicly available.

Space-time resolved inference-based neurophysiological process imaging: Application to resting-state alpha rhythm

Neural processes are complex and difficult to image. This paper presents a new space-time resolved brain imaging framework, called Neurophysiological Process Imaging (NPI), that identifies neurophysiological processes within cerebral cortex at the macroscopic scale. By fitting uncoupled neural mass models to each electromagnetic source time-series using a novel nonlinear inference method, population averaged membrane potentials and synaptic connection strengths are efficiently and accurately inferred and imaged across the whole cerebral cortex at a resolution afforded by source imaging. The efficiency of the framework enables return of the augmented source imaging results overnight using high performance computing. This suggests it can be used as a practical and novel imaging tool. To demonstrate the framework, it has been applied to resting-state magnetoencephalographic source estimates. The results suggest that endogenous inputs to cingulate, occipital, and inferior frontal cortex are essential modulators of resting-state alpha power. Moreover, endogenous input and inhibitory and excitatory neural populations play varied roles in mediating alpha power in different resting-state sub-networks. The framework can be applied to arbitrary neural mass models and has broad applicability to image neural processes of different brain states.

Extreme value theory for space-time with random coefficients

In this paper, we study the extreme value behavior of the space-time process given by X i ( t ) = ∑ j ≥ 1 Ψ ij ( t ) Z i − j ( t ) , t ∈ [ 0 , 1 ] , i ∈ Z . We assume that { Z i ( t ) } t ∈ [ 0 , 1 ] , i ∈ Z is a sequence of i.i.d random fields on [ 0 , 1 ] with values in the Skorokhod space D [ 0 , 1 ] of càdlàg functions (i.e., right-continuous functions with left limits) D [ 0 , 1 ] equipped with the J 1 topology. The coefficients { Ψ ij ( t ) } t ∈ [ 0 , 1 ] , i ∈ Z are processes with continuous sample paths. Our first aim is to establish a limit theory for point processes based on X(t). Secondly, using point processes, we study the limiting distribution of the normalized maximum process { a n − 1 max 1 ≤ i ≤ n X i ( t ) } t ∈ [ 0 , 1 ] . The result obtained in the second step can be viewed as extension of Balan who postponed the study of the behavior of maxima. It can also be considered as a generalization of Davis and Mikosch from deterministic real coefficients to random coefficients ( Ψ ij ) i ≥ 1 .

Efficiency of Penrose process in spacetime of axially symmetric magnetized Reissner-Nordström black hole

In this paper, we investigate the Penrose process in the purlieus of the axially symmetric magnetized Reissner-Nordstr\"{o}m black hole for both neutral and charged particles. We start with the study of the geometry of the black hole and find the regions where the $g_{tt}$ component of the metric tensor is positive (i.e., $g_{tt}>0$). It is interestingly found that the condition $g_{tt}>0$ is fulfilled not only close to the event horizon known as the ergosphere but also far away from the event horizon in the silhouette of potential wells. We also show that as the dimensionless magnetic field $B$ increases the silhouette of potential wells for which $g_{tt}>0$ grows correspondingly and eventually merges with the ergoregion when $B\gtrsim 1.6$. Finally, we investigate the efficiency of the Penrose process for the axially symmetric magnetized black hole case and bring out the effect of the magnetic field on it. Further, we also compare our results with the one for Kerr black hole. We show that when the charge $Q$ of the black hole is kept constant, the efficiency of the energy extraction process for the case of {a neutral particle (i.e., $q/m=0$) first increases and then begins to decrease with rise in the value of $B$ field, in contrast to Kerr black hole where it always increases as the rotation parameter grows. However, for the case of a charged particle (i.e., $q\neq 0$) the efficiency always increases with the rise in the $B$ field and can go over $100\%$, when both $B$ and $q/m$ are large enough (say $B\approx1$ and $q/m>2.2$)}. It is worth noting that the existence of regions away from the horizon where $g_{tt}>0$ also favors the energy-extraction process away from the effect of the black hole. However, the energy extraction from these regions is pure consequence of the magnetic field.

DOA estimation algorithm based on spread spectrum sequence in low signal-to-noise ratio

Spread spectrum communication is a common communication method in underwater communication. Based on the space-time processor received by the array, it can filter the signals arriving along each path separately. Combined with the diversity of space-time clusters, it can effectively improve the communication system’s reliability. The core problem of the space-time processor is the direction of arrival (DOA) and signal source number estimation. Based on the good self-coherence of the spread spectrum sequence, this paper proposes a multiple signal classification algorithm (MUSIC) for accurate DOA estimation. However, since the MUSIC algorithm uses the received signal’s covariance matrix for DOA estimation, the number of sources needs to be predicted in advance. Under a low signal-to-noise ratio (SNR), the signal eigenvalues and the noise eigenvalues of the covariance matrix differ slightly, which makes signal source number estimation difficult. To address this issue, a singular value decomposition method using the delay structure information of the array element is proposed to estimate the number of sources of the spreading sequence under a low SNR. The method proposed in this paper can well estimate the DOA of the signal under a low SNR. Meanwhile, there is no need to convert the signal to the individual sub-bands, which effectively reduces the calculation overhead. At the same time, the Hankel matrix is used to solve the problem that the MUSIC algorithm cannot accurately estimate the number of signal sources under the condition of low SNR. Compared with the conventional algorithm, the Hankel matrix can more accurately estimate the number of signal sources in the case of low SNR. Through simulation experiments, the effectiveness of our DOA estimation algorithm is validated under a low SNR.

Spatial analysis guiding decision making in environmental conservation: Systematic conservation planning and ecosystem services

Achieving conservation targets for sustainable development has been one of society’s greatest challenges. In this context, environmental conservation approaches such as Systematic Conservation Planning (SCP) and ecosystem services (ES) have become increasingly popular as feasible solutions for the allocation, delimitation, and management of protected areas. These approaches, often used to drive public policies based on payment for environmental services, have highlighted the intrinsic relationships between the paradigms of geography and spatial analysis (SA), as they rely on space-time processes and multidisciplinary concepts for the analysis of the biophysical, social, and economic variables. In this context, this manuscript aimed to outline the relevance of SA as a geographic perspective for the progress of environmental conservation. The arguments were here aligned in the following steps: (i) concepts around protected areas and the factors that impact them; (ii) environmental conservation approaches used to allocate and delimit protected areas, and their respective features, limitations, and related definitions; and (iii) correlations between SA and the use of ES and SCP (paradigms, advances, and contributions). As major findings, it was indicated that the SCP and ES work in a space-time dimension to measure and describe patterns of abstract phenomena using spatial analysis techniques. Moreover, we identified that conceptual mismatches and the absence of a common language to environmental conservation approaches reduces the expressive participation of geography that has its focus on determination the abstract features of observed objects or phenomena. It is important, however, that its paradigms become an essential methodological component in environmental approaches to the quantification and delimitation of the elements and natural processes.

동북아 원사시대의 물질문화변동

The new technologies of ceramic production, such as wheel-turning, paddling and firing in reducing-atmosphere, were widely diffused into the Northeastern periphery of East Asia during the Proto-historic period. As the Yan(燕) State established political and military outposts in the Liaoning area in the late Warring States period, new technologies accepted in the expanded territory also affected the ceramic production in the southern part of the Korean Peninsula. The previous researches on the transformation of ceramic production technology and organization in the Proto-historic period Northeast Asia have focused on the question of when and from where the technology was accepted, but this study attempted to explain it as a time-space process of the technological innovation. When the potters with new techniques and their production organizations migrated into a certain region, the contact with the potters from indigenous communities that produced ceramic wares in traditional methods took place. As potters based on different technological systems came into contact, the technology that had been introduced with the migration of potter was transmitted to the indigenous potters through the fabrication learning, and they acquired the new bodily techniques to produce traditional and new ceramic vessels. With these technological transformations, indigenous groups develop into a complex society and develop a new organization for the ceramic production. Along with these technological changes, a new specialized organization of ceramic production developed between indigenous communities. This paper attempted a comparative explanation of the processes of technological innovations in two different regions, the southeastern part of the Korean Peninsula and the upper Han-River valley, based on the agency theory.

A Spatially Correlated Model with Generalized Autoregressive Conditionally Heteroskedastic Structure for Counts of Crimes.

Crime is a negative phenomenon that affects the daily life of the population and its development. When modeling crime data, assumptions on either the spatial or the temporal relationship between observations are necessary if any statistical analysis is to be performed. In this paper, we structure space–time dependency for count data by considering a stochastic difference equation for the intensity of the space–time process rather than placing structure on a latent space–time process, as Cox processes would do. We introduce a class of spatially correlated self-exciting spatio-temporal models for count data that capture both dependence due to self-excitation, as well as dependence in an underlying spatial process. We follow the principles in Clark and Dixon (2021) but considering a generalized additive structure on spatio-temporal varying covariates. A Bayesian framework is proposed for inference of model parameters. We analyze three distinct crime datasets in the city of Riobamba (Ecuador). Our model fits the data well and provides better predictions than other alternatives.

A Mechanistic Model of Annual Sulfate Concentrations in the United States

Understanding how individual pollution sources contribute to ambient sulfate pollution is critical for assessing past and future air quality regulations. Since attribution to specific sources is typically not encoded in spatial air pollution data, we develop a mechanistic model which we use to estimate, with uncertainty, the contribution of ambient sulfate concentrations attributable specifically to sulfur dioxide (SO2) emissions from individual coal-fired power plants in the central United States. We propose a multivariate Ornstein–Uhlenbeck (OU) process approximation to the dynamics of the underlying space-time chemical transport process, and its distributional properties are leveraged to specify novel probability models for spatial data that are viewed as either a snapshot or time-averaged observation of the OU process. Using US EPA SO2 emissions data from 193 power plants and state-of-the-art estimates of ground-level annual mean sulfate concentrations, we estimate that in 2011—a time of active power plant regulatory action—existing flue-gas desulfurization (FGD) technologies at 66 power plants reduced population-weighted exposure to ambient sulfate by 1.97 μg/m3 (95% CI: 1.80–2.15). Furthermore, we anticipate future regulatory benefits by estimating that installing FGD technologies at the five largest SO2-emitting facilities would reduce human exposure to ambient sulfate by an additional 0.45 μg/m3 (95% CI: 0.33–0.54). Supplementary materials for this article are available online.

Increased Azimuth Resolution by Extrapolating the Antenna Array Aperture Function by Least Squares Linear Prediction Estimation Using Autoregressive Model Coefficients

Introduction. Short-wave stations for detecting airborne objects of the ionospheric type have a number of limited technical characteristics, one of which is their low azimuth resolution. This limitation is manifested in the impossibility to separately observe air objects in a group, the distance between which is less than 30 km (at an observation range of 2000 km). The technical characteristics under consideration can be improved by making changes to the dimensions of the receiving antenna array (AA); however, such changes lead, as a rule, to unjustified engineering and financial costs. In practice, space-time signal processing is carried out using conventional superresolution methods, which, although increasing the resolution of the station, decrease the rate of delivery of observation results to the operator due to an additional computational load. It is necessary to find a compromise between the maximum possible resolution indicator and the acceptable load on the system during signal processing.Aim. Analysis of the phase distribution of the incident wave scattered by objects at the AA aperture, as well as the azimuthal images of these objects when performing spacetime signal processing after extrapolating the AA aperture function by evaluating linear prediction using the least-squares method using autoregressive model coefficients.Materials and methods. Modelling of phase distributions at the AA aperture and azimuthal images of the observed objects was conducted in the MATLAB environment.Results. It is shown that the problem of increasing the azimuth resolution of a short-wave station for detecting air objects can be successfully solved using linear prediction based on the least-squares method using autoregressive model coefficients for the extrapolation of the AA aperture function. The results obtained during modelling were analysed using the example of group observation of air objects.Conclusion. The proposed approach for extrapolation of the AA aperture function for short-wave stations with large receiving AAs proved its relevance. The method proposed for increasing the resolution is characterized by a lower computational load, thereby being promising for practical application.

A data alternating extraction general structure and its algorithms for adaptive space–time wideband beamforming

The adaptive space-time processing is a well-substantiated method to build a wideband beamformer. This paper proposes a low complexity general structure for the adaptive space-time wideband beamforming. The new general structure uses a data alternating extraction approach for adjacent arrays to reduce the scales of received data, resulting in obtaining the optimum adaptive weights with much lower computational complexity. Then, the adaptive wideband beamforming algorithms are proposed for the general structure. Among the novel algorithms, the singular value decomposition alternating extraction frequency constrained Frost beamformer (SVD-AEFCFB) and SVD-AEFC Laguerre beamformer (SVD-AEFCLB) both have an undistorted response to the wideband signal of interest and satisfactory anti-interference capability with the lowest computational complexity. Importantly, the proposed structure and algorithms have the same generalized and popularized characteristics for the other space-time methods. Simulation results highlight the correctness and effectiveness of the proposed methods.

Space-Time Tree Search for Long-Term Trajectory Prediction

The pedestrian trajectory prediction is critical for autonomous driving, intelligent navigation, and abnormal behavior detection. With the booming of artificial intelligence (AI), many researchers have employed deep learning technologies to solve the pedestrian trajectory prediction problem and obtained relatively better performance in the short-term trajectory prediction. However, long-term trajectory prediction is still challenging to achieve high prediction accuracy. In this work, we propose a space-time tree search (STTS) method for long-term pedestrian trajectory prediction. Compared with existing methods only considering the problem from the space dimension, the proposed method formulates the trajectory prediction problem as a joint space-time tree search process by mapping the environment to a grid map. Since the human’s trajectory is relative to space and time dimensions, the trajectory prediction accuracy can be improved by the two dimensions. Then, a space-time reward trained neural network is employed to extract the pedestrian’s intent with both the scene image and the historical trajectory as input and outputs the prior search probabilities. Finally, the tree search can obtain the optimal predicted trajectory according to the prior probabilities, significantly improving the tree search efficiency. After testing, our proposed method can perform better than existing methods.

Aspects of practical implementation of space-time signal processing in adaptive antenna arrays in a complex electromagnetic environment

Aspects of practical implementation of space-time signal processing in adaptive antenna arrays in a complex electromagnetic environment

The Second Competition on Spatial Statistics for Large Datasets

In the last few decades, the size of spatial and spatio-temporal datasets in many research areas has rapidly increased with the development of data collection technologies. As a result, classical statistical methods in spatial statistics are facing computational challenges. For example, the kriging predictor in geostatistics becomes prohibitive on traditional hardware architectures for large datasets as it requires high computing power and memory footprint when dealing with large dense matrix operations. Over the years, various approximation methods have been proposed to address such computational issues, however, the community lacks a holistic process to assess their approximation efficiency. To provide a fair assessment, in 2021, we organized the first competition on spatial statistics for large datasets, generated by our ExaGeoStat software, and asked participants to report the results of estimation and prediction. Thanks to its widely acknowledged success and at the request of many participants, we organized the second competition in 2022 focusing on predictions for more complex spatial and spatio-temporal processes, including univariate nonstationary spatial processes, univariate stationary space-time processes, and bivariate stationary spatial processes. In this paper, we describe in detail the data generation procedure and make the valuable datasets publicly available for a wider adoption. Then, we review the submitted methods from fourteen teams worldwide, analyze the competition outcomes, and assess the performance of each team.

Iterative Equalization of STSK-Aided Single-Carrier Systems: Design and Analysis With Non-Ideal Power Amplifiers

A plethora of new applications will be enabled by next generation wireless networks. These demand sophisticated medium access control and physical layer protocols in order to fulfill the requirements in terms of spectral efficiency, bit error rate (BER), energy efficiency, reliability, and security, among others. This paper develops an advanced space-time multiple-input, multiple-output (MIMO) solution that builds on the concept of space-time shift keying (STSK) for single-carrier frequency domain equalization (SC-FDE). STSK-aided SC-FDE, or SSF, ensures space-time and frequency diversity in dispersive channels, but can only support suboptimal linear receiver schemes. We therefore introduce a turbo equalizer for SSF in frequency-selective multipath channels. It enables inter-symbol-interference cancellation by means of iterative space-time processing. We coin it the iterative space-time block equalizer (SSF-ISTBE). We provide the mathematical formulation for the proposed SSF-ISTBE framework in non-linear channel conditions. We demonstrate that the SSF-ISTBE achieves a substantial 4 dB gain in terms of BER over the linear minimum mean square error equalizer while significantly reducing the computational complexity of the optimal maximum likelihood SSF. When considering practical transceivers, in particular non-linear power amplifiers, the gain of the SSF-ISTBE becomes more prominent over state-of-the-art approaches, demonstrating its robustness against non-linear distortion.

Моделирование пассивных помех в РЛС с цифровой антенной решеткой

The problem of imitation modeling of passive interference is considered in the context of devel-oping algorithms for space-time processing of broadband signals in a radar system with a digital antenna array. Spectral and correlation characteristics of passive noise are discussed in the frame-work of Gaussian, polynomial (fractional rational) and exponential models. Due to the broadband nature of probing signals, the formation of the antenna array directional pattern is carried out by controlling the time delays of oscillations arriving at the array elements, which is implemented in the frequency domain through complex weighting factors. Modeling of passive interference as a sta-tionary complex random process is performed in discrete "slow" time, with each complex vector of samples being spaced from neighboring vectors by the value of the repetition period of the probing pulses. The known method for modeling passive interference based on a shaping recursive filter is unsuitable for use in the case of an interference spectrum other than rational. The method for model-ing the vector of correlated samples using the Cholesky expansion of the noise covariance matrix is inapplicable when the correlation coefficient is close to one. A method is proposed for modeling masking extended passive interference of a wide class based on filtering a sequence of complex pseudo-random vectors in the frequency domain with the use of a fast Fourier transform.

The Event Ontology of Nature

We propose a new event ontology of the world, which is part of a general approach to philosophy based on combining ideas from science, ontology, and the philosophy of nature. While the position advocated here is grounded in science and philosophy, it attempts to move beyond each of them by devising and exploring a series of technical (naturalized or naturalistic) ontological concepts such as Interconnectedness, the Whole, the Global, Chaos, the event assemblage, and Nonspace. A central theme in our event ontology is the mapping out of a fundamental critique of the theory of the organism and organization, especially when the latter two are viewed as processes in spacetime. In particular, and following earlier leads, we criticize the spacetime doctrine by arguing that it is not ontologically fundamental, where we suggest its replacement by more primordial naturalized ontological concepts of space such as ontospace and Nonspace. The event ontology of nature can be considered a radical alternative attitude toward the relation between the human and nature, an attitude, in fact, that has been repeatedly explored, though under very different headings, by numerous scattered thinkers throughout the history of ideas. We examine some of the past thinkers who contributed to this general but still incoherent body of thought, including Leibniz, Heidegger, Simondon, Ruyer, Deleuze, Whitehead, and Guattari. The goal of this article is to provide a condensed high-level view on this very complex and still evolving subject intended for a large audience, not necessarily only philosophers, but also scientists, mathematicians, technologists, theologians, sociologists, artists, and psychologists.

Analysis of quasi-periodic space-time non-separable processes to support decision-making in medical monitoring systems

In many decisionsupport systemsthere are processedchaotic spatial-time processes which are non-separable and quasi-periodic. Some examples of such systemsareepidemic spreading, population development, fire spreading, radio wave signals, image processing, information encryption, radio vision, etc. Processes in these systems have periodic character, e.g. seasonal fluctuations(epidemic spreading, population development), harmonic fluctuations (pattern recognition, image processing),etc. In simulation block the existing systems use separable process models which are presented as multiplication of spatialand temporal parts and are linearized. This significantly reduces the quality of spatial-time non-separable processes. The quality model building of chaotic spa-tial-time non-separable processwhich is processed by decisionsupport systemis necessary for getting of learning set. Itis really complicated especially if the random process is formed. The implementation ensemble of chaotic spatial-time non-separable process requires high costs what causes reduction of the system efficiency. Moreover, in many cases the implementation ensemble of spatial-time processes is impossible to get. In this workthemathematical model of a quasi-periodic spatial-time non-separable process has been developed. Based on it the formation method of this process has been developed and investigated. The epidemic spreading pro-cessed was presented as an example.