Spatial-temporal Localization Research Articles

Denoising of Dense Spatial Array Data Using the Curvelet Transform

ABSTRACT To remove background noise from seismic data recorded by spatially dense arrays, we have developed a space-based denoising procedure using the discrete curvelet transform. Based on a detailed statistical characterization of noise coefficients through the empirical cumulative distribution function method within a pre-event time window, signal and noise can be separated effectively by nonlinear thresholding. After synthetic test, we applied this method on data from an industry 3D seismic experiment recorded at an array deployed near Utica, Ohio. The denoising results show good waveform consistency with a significantly enhanced signal-to-noise ratio. Our curvelet approach allows a more computationally efficient spatial–temporal localization analysis of seismic data than conventional curvelet techniques by avoiding the assumption of stationary Gaussian-distributed noise and can be implemented as a complement of time-domain wavelet methods with fewer frequency losses after denoising. This new method provides a fast and convenient way to recover signals from noisy recordings with dense 2D arrays, leading to a considerable improvement in data quality compared with conventional Fourier, wavelet, and curvelet methods. The partitioned seismic signals and noise would yield advanced earth structure imaging, small-event detection, ambient noise tomography, and others.

Designing a Human-centered AI Tool for Proactive Incident Detection Using Crowdsourced Data Sources to Support Emergency Response

Time of incident reporting is a critical aspect of emergency response. However, the conventional approaches to receiving incident reports have time delays. Non-traditional sources such as crowdsourced data present an opportunity to detect incidents proactively. However, detecting incidents from such data streams is challenging due to inherent noise and data uncertainty. Naively maximizing detection accuracy can compromise spatial-temporal localization of inferred incidents, hindering response efforts. This article presents a novel human-centered AI tool to address the above challenges. We demonstrate how crowdsourced data can aid incident detection while acknowledging associated challenges. We use an existing CROME framework to facilitate training and selection of best incident detection models, based on parameters suited for deployment. The human-centered AI tool provides a visual interface for exploring various measures to analyze the models for the practitioner’s needs, which could help the practitioners select the best model for their situation. Moreover, in this study, we illustrate the tool usage by comparing different models for incident detection. The experiments demonstrate that the CNN-based incident detection method can detect incidents significantly better than various alternative modeling approaches. In summary, this research demonstrates a promising application of human-centered AI tools for incident detection to support emergency response agencies.

Structural entropy and spatial decay of quasimodes in Vogel spirals

We investigate the spatial decay and temporal localization properties of quasimodes (i.e., scattering resonances) of two-dimensional Vogel spirals, composed of deterministic, aperiodic arrays of electric dipoles. By determining the structural entropy and localization maps of Vogel spirals using the Green's matrix method, we show that three distinctive decay types of quasimodes coexist in Vogel spirals: exponential, power-law, and Gaussian. While the exponential and the power-law decays typically occur in disordered media and multifractal systems, respectively, the Gaussian decay is demonstrated to characterize, on average, the most localized quasimodes of Vogel spirals, both spatially (smallest participation ratios) and temporarily (longest lifetimes). These decay forms are demonstrated by a no-fitting analysis of the localization maps, independently corroborated by calculating the electric field in real space, which also provides a direct evidence of the algebraic spatial decay of critical quasimodes. Altogether our findings unveil a rich spectrum of both long-lived and spatially localized quasimodes that coexist in Vogel spirals and can be of direct relevance to novel optical functionalities for applications to light sources and sensing devices.

Brown Measure Based Spectral Distribution Analysis for Spatial-Temporal Localization of Cascading Events in Power Grids

Real-time detection and analysis of cascading events are crucial to avoiding large blackouts in power systems. Based on spectral distribution analysis (SDA) of online monitoring data, this article proposes an approach for spatial-temporal localization of cascading events in a modern grid. The anomaly detection problem is built upon hypothesis testing, where a test statistic is designed in advance. The empirical spectral distribution (ESD) of the test statistic is compared with its theoretical counterpart, i.e., the asymptotic spectral distribution (ASD) obtained by Brown Measure. The proposed approach is sensitive and capable of temporally locating the occurrence time of each subevent (including severe disturbances, failures, and some typical physical attacks) in a cascading event. Simultaneously, the spatial information of each subevent is given. It is experimentally justified that the proposed approach is robust to normal fluctuations, oscillations, and bad data. Besides, it can be applied in both large-scale and small-scale systems utilizing the tensor product method. Case studies with simulated data in an ACTIVSg500 System and real-world online monitoring data verify the effectiveness of the proposed approach.

An Ego-Vision System for Discovering Human Joint Attention

Joint attention often happens during social interactions, in which individuals share focus on the same object. This article proposes an egocentric vision-based system (ego-vision system) that aims to discover the objects looked at jointly by a group of persons engaged in interactive activities. The proposed system relies on a collection of wearable eye-tracking cameras that provide an egocentric view of the interaction scenes as well as points-of-gaze measurement of each participant. Technically in our system, we develop a hierarchical conditional random field (CRF) based graphical model that can temporally localize joint attention periods and spatially segment objects of joint attention. By solving these two coupled tasks together in an iterative optimization procedure, we show that human joint attention can be reliably discovered from videos even with cluttered background and noisy gaze measurement. A new dataset of joint attention is collected and annotated for evaluating the two tasks of joint attention where two to four persons are involved. Experimental results demonstrate that our approach achieves state-of-the-art performance on both tasks of spatial segmentation and temporal localization of joint attention.

Progressive Cross-Stream Cooperation in Spatial and Temporal Domain for Action Localization

Spatio-temporal action localization consists of three levels of tasks: spatial localization, action classification, and temporal localization. In this work, we propose a new progressive cross-stream cooperation (PCSC) framework that improves all three tasks above. The basic idea is to utilize both spatial region (resp., temporal segment proposals) and features from one stream (i.e., the Flow/RGB stream) to help another stream (i.e., the RGB/Flow stream) to iteratively generate better bounding boxes in the spatial domain (resp., temporal segments in the temporal domain). In this way, not only the actions could be more accurately localized both spatially and temporally, but also the action classes could be predicted more precisely. Specifically, we first combine the latest region proposals (for spatial detection) or segment proposals (for temporal localization) from both streams to form a larger set of labelled training samples to help learn better action detection or segment detection models. Second, to learn better representations, we also propose a new message passing approach to pass information from one stream to another stream, which also leads to better action detection and segment detection models. By first using our newly proposed PCSC framework for spatial localization at the frame-level and then applying our temporal PCSC framework for temporal localization at the tube-level, the action localization results are progressively improved at both the frame level and the video level. Comprehensive experiments on two benchmark datasets UCF-101-24 and J-HMDB demonstrate the effectiveness of our newly proposed approaches for spatio-temporal action localization in realistic scenarios.

Tissue-specific Fixation Methods Are Required for Optimal In Situ Visualization of Hyaluronan in the Ovary, Kidney, and Liver.

Hyaluronan (HA) is a ubiquitous component of the extracellular matrix. The spatial-temporal localization of HA can be visualized in situ using biotinylated HA binding proteins (HABPs). This assay is sensitive to fixation conditions, and there are currently no best practices for HA detection. Thus, the goal of this study was to optimize fixation conditions for visualizing HA in the ovary, kidney, and liver through analysis of six commonly used fixatives for HA detection: Bouin's Solution, Carnoy's Solution, Ethanol-Formalin-Glacial Acetic Acid (EFG), Histochoice, Modified Davidson's Solution, and 10% Neutral Buffered Formalin. Organs were harvested from CB6F1 mice and fixed with one of the identified fixatives. Fixed organs were sectioned, and the HABP assay was performed on sections in parallel. Hematoxylin and eosin staining was also performed to visualize tissue architecture. HABP signal localization and intensity varied between fixatives. EFG and Carnoy's Solution best preserved the HA signal intensity in the ovary and liver, showing HA localization in various sub-organ structures. In the kidney, only Modified Davidson's Solution was less than optimal. Our findings demonstrate that fixation can alter the ability to detect HA in tissue macro- and microstructures, as well as localization in a tissue-specific manner, in situ.

Joint Estimation of Effective Brain Wave Activation Modes Using EEG/MEG Sensor Arrays and Multimodal MRI Volumes.

In this letter, we present a new method for integration of sensor-based multifrequency bands of electroencephalography and magnetoencephalography data sets into a voxel-based structural-temporal magnetic resonance imaging analysis by utilizing the general joint estimation using entropy regularization (JESTER) framework. This allows enhancement of the spatial-temporal localization of brain function and the ability to relate it to morphological features and structural connectivity. This method has broad implications for both basic neuroscience research and clinical neuroscience focused on identifying disease-relevant biomarkers by enhancing the spatial-temporal resolution of the estimates derived from current neuroimaging modalities, thereby providing a better picture of the normal human brain in basic neuroimaging experiments and variations associated with disease states.

Active Electro-Location of Objects in the Underwater Environment Based on the Mixed Polarization Multiple Signal Classification Algorithm.

This article proposes a novel active localization method based on the mixed polarization multiple signal classification (MP-MUSIC) algorithm for positioning a metal target or an insulator target in the underwater environment by using a uniform circular antenna (UCA). The boundary element method (BEM) is introduced to analyze the boundary of the target by use of a matrix equation. In this method, an electric dipole source as a part of the locating system is set perpendicularly to the plane of the UCA. As a result, the UCA can only receive the induction field of the target. The potential of each electrode of the UCA is used as spatial-temporal localization data, and it does not need to obtain the field component in each direction compared with the conventional fields-based localization method, which can be easily implemented in practical engineering applications. A simulation model and a physical experiment are constructed. The simulation and the experiment results provide accurate positioning performance, with the help of verifying the effectiveness of the proposed localization method in underwater target locating.

Multiple Signal Classification Algorithm Based Electric Dipole Source Localization Method in an Underwater Environment

A novel localization method based on multiple signal classification (MUSIC) algorithm is proposed for positioning an electric dipole source in a confined underwater environment by using electric dipole-receiving antenna array. In this method, the boundary element method (BEM) is introduced to analyze the boundary of the confined region by use of a matrix equation. The voltage of each dipole pair is used as spatial-temporal localization data, and it does not need to obtain the field component in each direction compared with the conventional fields based localization method, which can be easily implemented in practical engineering applications. Then, a global-multiple region-conjugate gradient (CG) hybrid search method is used to reduce the computation burden and to improve the operation speed. Two localization simulation models and a physical experiment are conducted. Both the simulation results and physical experiment result provide accurate positioning performance, with the help to verify the effectiveness of the proposed localization method in underwater environments.

Experimental investigation of the spatio-temporal localization of deformation and damage in sylvinite specimens under uniaxial tension

In this work, the digital image correlation method and the acoustic emission technique are used to study the spatial–temporal localization of deformation in sylvinite under direct uniaxial tensile loading. The distribution of local strain fields over the lateral surface of the specimen subjected to quasistatic tension is analyzed. It has been found that the process of deformation in sylvinite specimens under quasi-static tension, proceeds as a sequence of two forms of spatial–temporal localization: a system of equidistant stationary areas of localized strain field and a single stationary dissipative localized structure, in which the deformation grows in an avalanche-like manner and ends in macro-fracture. The transition from one localization form to another occurs around the highest possible stress and is accompanied by a sharp decrease in the concentration parameter. The concentration parameter characterizes the degree of interaction between the defects at different scale levels through their elastic fields and can be estimated from the acoustic emission data.

Methodological proposals to handle imperfect spatial and temporal information in the context of natural hazard studies

Natural hazard analysis often makes use of alphanumeric data sets whose content is mainly expressed using natural language expressions that describe where and when events such as eruptions, avalanches, floods , etc. took place. However, a lot of expressions used in natural language are vague and imprecise and especially those which are used to locate places (around, near to, north of, etc.) and dates (between 1710 and 1711, at the beginning of the century, etc.). Some works propose approaches to define a spatial expansion representing the localization of any vague spatial information. Yet, these approaches do not allow to define any spatial or temporal localization that takes into account their physical environment: topographical or meteorological elements of the studied ground, nature and the characteristics of the observed phenomenon, etc. We propose a methodology that allows to improve the localization and the cartographic representation of imperfect spatial and temporal information. This methodology relies on a specific extension of Spatial ML and TimeML markup languages and an ontology for the integration, quantification and representation of imperfect spatio-temporal information expressed in natural language in order to locate natural hazard phenomena. In this paper, we present the elements of the STELLA (Spatial - TEmporal LocaLizAtion) methodology and framework .

Porphyry Cu (–Mo–Au) deposits related to melting of thickened mafic lower crust: Examples from the eastern Tethyan metallogenic domain

Most porphyry Cu deposits in the world occur in magmatic arc settings and are formed in association with calc-alkaline arc magmas related to subduction of oceanic lithosphere. This contribution reviews a number of significant porphyry Cu deposits in the eastern Tethyan metallogenic domain. They widely occur in a variety of non-arc settings, varying from post (late)-collisional transpressional and extensional environments to intracontinental extensional environments related to orogenic and anorogenic processes. Their spatial–temporal localization is controlled by strike–slip faults, orogen-transverse normal faults, lineaments and their intersections in these non-arc settings. These deposits are dominated by porphyry Cu–Mo deposits with minor porphyry Cu–Au and epithermal Au deposits, and exhibit a broad similarity with those in magmatic arcs. The associated magmas are generally hydrous, relatively high fO 2, high-K calc-alkaline and shoshonitic, and show geochemical affinity with adakites. They are distinguished from arc magmas and/or oceanic-slab derived adakites, by their occurrence as isolated complexes, high K 2O contents (1.2–8.5%), and much wider range of ε Nd(t) values(− 10 to + 3) and positive ε Hf(t) values (+ 4.6 to + 6.9). These potassic magmas are most likely formed by partial melting of thickened juvenile mafic lower-crust or delaminated lower crust, but also involving various amounts of asthenospheric mantle components. Key factors that generate hydrous fertile magmas are most likely crust/mantle interaction processes at the base of thickened lower-crust in non-arc settings, rather than oceanic-slab dehydration (as in arc settings). Breakdown of amphibole in thickened lower crust (e.g., amphibole eclogite and garnet amphibolite) during melting is considered to release fluids into the fertile magmas, leading to an elevated oxidation state and higher H 2O content necessary for development of porphyry Cu–Mo–Au systems. Copper and Au in hydrous magmas are likely derived from mantle-derived components and/or melts, which either previously underplated and infiltrated at the base of the thickened lower crust, or were input into the primitive magmas by melt/mantle interaction. In contrast, Mo and (part of the) S in the fertile magmas are probably supplied by old crust during melting and subsequent ascent.

Development of a micro droplet collider; the liquid–liquid system utilizing the spatial–temporal localized energy

We introduce the liquid–liquid system to utilize both of the spatial and the temporal localized energy simultaneously by adding temporal localization to microfluidics. Liquid droplets in the gas phase confined in microchannels are used for spatial–temporal localization to realize various chemical processes. A highly accelerated droplet generates the high kinetic energy and momentum in the spatial localization. “Rapid” collision caused by the highly accelerated droplet enables the spatial and temporal localization of the high-energy, which induces the conversion and transfer of the high-energy. We refer to this new microfluidics system as a “micro droplet collider.” We fabricated launchers for the droplet shot, tracks for the droplet run, and a chamber for the collision between two droplets on a microchip based on gas–liquid Laplace pressure. The acceleration of the droplet to the high velocity (106 μm s−1; 102 times faster velocity and 104 times higher energy than that of typical water-in-oil droplet format), rapid collision (<100 μs) at a small interface between droplets were achieved. Inelastic and minimally deformable collision was conducted, which enabled the utilization of the localized high-energy efficiently. As one application to chemical processes, we demonstrated rapid mixing (0.5 s) between two droplets (volume ratio; 1 (0.25 nl):10), a process that has not been achieved yet. Mixing time was 6,000 times faster than that of molecular diffusive mixing by utilizing the localized high-energy. The developed micro droplet collider is expected to contribute greatly to microfluidics and chemical processes on a microchip.

Late gestational lung hypoplasia in a mouse model of the Smith-Lemli-Opitz syndrome.

BackgroundNormal post-squalene cholesterol biosynthesis is important for mammalian embryonic development. Neonatal mice lacking functional dehydrocholesterol Δ7-reductase (Dhcr7), a model for the human disease of Smith-Lemli-Opitz syndrome, die within 24 hours of birth. Although they have a number of biochemical and structural abnormalities, one cause of death is from apparent respiratory failure due to developmental pulmonary abnormalities.ResultsIn this study, we characterized further the role of cholesterol deficiency in lung development of these mice. Significant growth retardation, beginning at E14.5~E16.5, was observed in Dhcr7-/- embryos. Normal lobation but smaller lungs with a significant decrease in lung-to-body weight ratio was noted in Dhcr7-/- embryos, compared to controls. Lung branching morphogenesis was comparable between Dhcr7-/- and controls at early stages, but delayed saccular development was visible in all Dhcr7-/- embryos from E17.5 onwards. Impaired pre-alveolar development of varying severity, inhibited cell proliferation, delayed differentiation of type I alveolar epithelial cells (AECs) and delayed vascular development were all evident in knockout lungs. Differentiation of type II AECs was apparently normal as judged by surfactant protein (SP) mRNAs and SP-C immunostaining. A significant amount of cholesterol was detectable in knockout lungs, implicating some maternal transfer of cholesterol. No significant differences of the spatial-temporal localization of sonic hedgehog (Shh) or its downstream targets by immunohistochemistry were detected between knockout and wild-type lungs and Shh autoprocessing occurred normally in tissues from Dhcr7-/- embryos.ConclusionOur data indicated that cholesterol deficiency caused by Dhcr7 null was associated with a distinct lung saccular hypoplasia, characterized by failure to terminally differentiate alveolar sacs, a delayed differentiation of type I AECs and an immature vascular network at late gestational stages. The molecular mechanism of impaired lung development associated with sterol deficiency by Dhcr7 loss is still unknown, but these results do not support the involvement of dysregulated Shh-Patched-Gli pathway in causing this defect.

Molecular dynamics simulations of strongly coupled plasmas: Localization and microscopic dynamics

The spatial–temporal localization of particles in the local minima of the potential surface is a prominent feature of strongly coupled plasmas. The duration of localization is investigated by molecular dynamics simulation, through monitoring of the decorrelation of the surroundings of individual particles. Three- and two-dimensional systems of particles interacting through Coulomb and Yukawa potentials are studied over a wide range of the plasma coupling (Γ) and screening (κ) parameters in the liquid phase. The oscillation spectrum of the caged particles in the equilibrium system as well as in the frozen environment of other particles (Einstein frequency spectrum) is determined.