Spatiotemporal Similarity Research Articles

Influence of hydraulic retention time and amplitude of water level fluctuations on water quality in irrigation reservoirs of the Kala Oya river basin, Sri Lanka

Management of reservoir water quality is a global challenge due to the natural process of eutrophication and anthropogenic aggravation. In Sri Lanka, irrigation reservoirs support several secondary uses such as fish production, livestock farming, and many communal uses including drinking water supply. In the present study, basic limnological parameters of ten irrigation reservoirs of Sri Lanka were investigated from June 2013 to February 2016, with a view to identifying influence of hydrological regimes on reservoir water quality. Spatio-temporal similarities of water quality parameters were studied employing the self-organizing map (SOM) routine of the artificial neural network application. The sample vectors, classified on the SOM lattice, indicated 6 clusters at 50% similarity level. When reservoir that were categorized according to hydraulic retention time (HRT) and relative reservoir level fluctuation (RRWL; defined as the ratio of the mean reservoir level amplitude to mean depth), were compared with dominant water quality parameters in SOM lattice, it was evident that some productivity-related water quality parameters were influenced HRT and RRWL. The results of the study revealed that HRT and RRWL can essentially be controlled through management of hydrological regimes in irrigation reservoirs, thus, close dialogue between irrigation authorities and other users of reservoir water resources are needed to ensure desired water quality of the reservoirs.

A model for measuring activity similarity between public transit passengers using smart card data

An activity is characterized by its location, time and type. Smart card data include the location and time of boarding and/or alighting transactions within the public transit system. This data can be used to study the spatiotemporal range of the activity as it usually happens between an alighting and the next boarding transaction. This kind of activity can also be inferred from the start time and duration of the activity, and the available land use in the vicinity. This paper proposes a model which considers the three main characteristics of the activity to measure similarities between passengers’ activities. The model consists of two parallel steps—one for the spatiotemporal aspects and the other for the activity type. The first one uses the concept of Space Time Prism (STP) to measure the spatiotemporal similarity of two activities in a three-dimensional continuous space. The latter models the activity type using a probabilistic decision tree to measure the activity type similarity. The final activity similarity value is the product of the activity type and the spatiotemporal similarity values. The model is implemented for four-day smart card data in Brisbane, Queensland [Australia]. In order to confirm the results of the model, the passengers are clustered and discussed based on the measured activity similarity. The results show more than 81 per cent of the passengers have partial or complete activity similarity with their fellow passengers.

Dynamic and Cell-Specific DACH1 Expression in Human Neocortical and Striatal Development.

DACH1 is the human homolog of the Drosophila dachshund gene, which is involved in the development of the eye, nervous system, and limbs in the fly. Here, we systematically investigate DACH1 expression patterns during human neurodevelopment, from 5 to 21 postconceptional weeks. By immunodetection analysis, we found that DACH1 is highly expressed in the proliferating neuroprogenitors of the developing cortical ventricular and subventricular regions, while it is absent in the more differentiated cortical plate. Single-cell global transcriptional analysis revealed that DACH1 is specifically enriched in neuroepithelial and ventricular radial glia cells of the developing human neocortex. Moreover, we describe a previously unreported DACH1 expression in the human striatum, in particular in the striatal medium spiny neurons. This finding qualifies DACH1 as a new striatal projection neuron marker, together with PPP1R1B, BCL11B, and EBF1. We finally compared DACH1 expression profile in human and mouse forebrain, where we observed spatio-temporal similarities in its expression pattern thus providing a precise developmental description of DACH1 in the 2 mammalian species.

Large-Area Gap Filling of Landsat Reflectance Time Series by Spectral-Angle-Mapper Based Spatio-Temporal Similarity (SAMSTS)

Landsat time series commonly contain missing observations, i.e., gaps, due to the orbit and sensing geometry, data acquisition strategy, and cloud contamination. A spectral-angle-mapper (SAM) based spatio-temporal similarity (SAMSTS) gap-filling algorithm is presented that is designed to fill small and large area gaps in Landsat data, using one year or less of data and without using other satellite data. Each gap pixel is filled by an alternative similar pixel that is located in a non-missing region of the image. The alternative similar pixel locations are identified by comparison of reflectance time series using a SAM metric revised to be adaptive to missing observations. A time series segmentation-and-clustering approach is used to increase the search efficiency. The SAMSTS algorithm is demonstrated using six months of Landsat 8 Operational Land Imager (OLI) reflectance time series over three 150 × 150 km (5000 × 5000 30 m pixels) areas in California, Minnesota and Kansas. The three areas contain different land cover types, especially crops that have different phenology and abrupt changes due to agricultural harvesting, which make gap filling challenging. Fillings on simulated gaps, which are equivalent to 36% of 5000 × 5000 images in each test area, are presented. The gap filling accuracy is assessed quantitatively, and the SAMSTS algorithm is shown to perform better than the simple closest temporal pixel substitution gap filling approach and the sinusoidal harmonic model-based gap filling approach. The SAMSTS algorithm provides gap-filled data with five-band reflective-wavelength root-mean-square differences less the 0.02, which is comparable to the OLI reflectance calibration accuracy.

Great ape walking kinematics: Implications for hominoid evolution.

Great apes provide a point of reference for understanding the evolution of locomotion in hominoids and early hominins. We assessed (1) the extent to which great apes use diagonal sequence, diagonal couplet gaits, like other primates, (2) the extent to which gait and posture vary across great apes, and (3) the role of body mass and limb proportions on ape quadrupedal kinematics. High-speed digital video of zoo-housed bonobos (Pan paniscus, N = 8), chimpanzees (Pan troglodytes, N = 13), lowland gorillas (Gorilla gorilla, N = 13), and orangutans (Pongo spp. N = 6) walking over-ground at self-selected speeds were used to determine the timing of limb touch-down, take-off, and to measure joint and segment angles at touch-down, midstance, and take-off. The great apes in our study showed broad kinematic and spatiotemporal similarity in quadrupedal walking. Size-adjusted walking speed was the strongest predictor of gait variables. Body mass had a negligible effect on variation in joint and segment angles, but stride frequency did trend higher among larger apes in analyses including size-adjusted speed. In contrast to most other primates, great apes did not favor diagonal sequence footfall patterns, but exhibited variable gait patterns that frequently shifted between diagonal and lateral sequences. Similarities in the terrestrial walking kinematics of extant great apes likely reflect their similar post-cranial anatomy and proportions. Our results suggest that the walking kinematics of orthograde, suspensory Miocene ape species were likely similar to living great apes, and highlight the utility of videographic and behavioral data in interpreting primate skeletal morphology.

Peat People. On the Function and Context of Medieval Artificial Platforms in a Coastal Wetland, Eelder- and Peizermaden, the Netherlands

ABSTRACTLarge, artificial habitation platforms are widely distributed along the southern North Sea coasts. In this article, we will focus on the less known, small medieval platforms situated further inland, on the edge of the extensive peat bogs which until medieval times covered much of the lowlands of the Netherlands. We have confronted data from the area Eelder- and Peizermaden with existing models on the dynamics of human colonization and exploitation of North Sea coastal wetlands. The evidence from ‘peat terps’ seems to reflect distinct and successive land-use types or strategies that are associated with at least two colonization phases. Changes in land use are related to environmental changes that are largely human-induced. This is typical for coastal peat landscapes. Land-use patterns display spatio-temporal similarities as well as differences. The observed succession of land-use types and the associated material culture bear a strong resemblance to developments in the Assendelver polder a millennium before.

The temporal niche and seasonal differences in predation risk to translocated and resident woodland caribou (Rangifertaranduscaribou)

Mountain caribou are an endangered ecotype of woodland caribou (Rangifer tarandus caribou (Gmelin, 1788)) that continue to decline, ultimately, due to habitat loss and, proximately, due to predation. A particularly imperilled population of mountain caribou was experimentally augmented with 19 northern caribou, a geographically distinct ecotype, from northern British Columbia. We examined seasonal variation in risk of predation by cougars (Puma concolor (L., 1771)) to the translocated caribou with comparison to resident caribou. Our basic approach followed the Movement Ecology Paradigm, in particular the interplay among why move, and when and where to move. We applied a cluster analysis framework on space-use patterns of GPS radio-collared animals to determine biologically relevant seasons. Then we examined the spatiotemporal similarity in habitat use between caribou groups and cougars across these seasons. This analysis included a control group of caribou from the donor herd that were not translocated. Five resident caribou seasons, two donor caribou seasons, and two cougar seasons were identified. Resident caribou remained at high elevations year-round and primarily selected habitats not used by cougars. In contrast, translocated caribou tended to occupy low-elevation habitats extensively used by cougars, resulting in predation of eight translocated caribou, six of which were by cougars. We concluded that the translocated caribou did not adopt the predator avoidance strategies of resident caribou, rendering them more vulnerable to predation. We make recommendations for future herd augmentations, notably that donor caribou should be of the same ecotype, have similar seasonal patterns of habitat use and associated behavioural repertoires, and be exposed to the same complex of predators.

Trajectory similarity join in spatial networks

The matching of similar pairs of objects, called similarity join, is fundamental functionality in data management. We consider the case of trajectory similarity join (TS-Join), where the objects are trajectories of vehicles moving in road networks. Thus, given two sets of trajectories and a threshold θ , the TS-Join returns all pairs of trajectories from the two sets with similarity above θ . This join targets applications such as trajectory near-duplicate detection, data cleaning, ridesharing recommendation, and traffic congestion prediction. With these applications in mind, we provide a purposeful definition of similarity. To enable efficient TS-Join processing on large sets of trajectories, we develop search space pruning techniques and take into account the parallel processing capabilities of modern processors. Specifically, we present a two-phase divide-and-conquer algorithm. For each trajectory, the algorithm first finds similar trajectories. Then it merges the results to achieve a final result. The algorithm exploits an upper bound on the spatiotemporal similarity and a heuristic scheduling strategy for search space pruning. The algorithm's per-trajectory searches are independent of each other and can be performed in parallel, and the merging has constant cost. An empirical study with real data offers insight in the performance of the algorithm and demonstrates that is capable of outperforming a well-designed baseline algorithm by an order of magnitude.

Online Detection of Low-Quality Synchrophasor Measurements: A Data-Driven Approach

In this paper, an online data-driven approach is proposed for the detection of low-quality synchrophasor measurements. The proposed method leverages the spatio-temporal similarities among multiple-time-instant synchrophasor measurements and formulates the low-quality synchrophasor data as spatio-temporal outliers. A density-based local outlier detection technique is proposed to detect the spatio-temporal outliers. This data-driven approach involves no system modeling information. The detection algorithm can operate under both normal and fault-on system conditions, with fast computation speed suitable for online applications. Case studies on both synthetic and real-world synchrophasor data verify the effectiveness of the proposed approach.

Low-frequency oscillations employ a general coding of the spatio-temporal similarity of dynamic faces

Brain networks use neural oscillations as information transfer mechanisms. Although the face perception network in occipitotemporal cortex is well-studied, contributions of oscillations to face representation remain an open question. We tested for links between oscillatory responses that encode facial dimensions and the theoretical proposal that faces are encoded in similarity-based “face spaces”. We quantified similarity-based encoding of dynamic faces in magnetoencephalographic sensor-level oscillatory power for identity, expression, physical and perceptual similarity of facial form and motion. Our data show that evoked responses manifest physical and perceptual form similarity that distinguishes facial identities. Low-frequency induced oscillations (< 20Hz) manifested more general similarity structure, which was not limited to identity, and spanned physical and perceived form and motion. A supplementary fMRI-constrained source reconstruction implicated fusiform gyrus and V5 in this similarity-based representation. These findings introduce a potential link between “face space” encoding and oscillatory network communication, which generates new hypotheses about the potential oscillation-mediated mechanisms that might encode facial dimensions.

A deep dive into location-based communities in social discovery networks

Location-based social discovery networks (LBSD) is an emerging category of location-based social networks (LBSN) that are specifically designed to enable users to discover and communicate with nearby people. In this paper, we present the first measurement study of the characteristics and evolution of location-based communities which are based on a social discovery network and geographic proximity. We measure and analyse more than 176K location-based communities with over 1.4 million distinct members of a popular social discovery network and more than 46 million locations. We characterise the evolution of the communities and study the user behaviour in LBSD by analysing the mobility features of users belonging to communities in comparison to non-community members. Using observed spatio-temporal similarity features, we build and evaluate a classifier to predict location-based community membership solely based on user mobility information.

Video super-resolution reconstruction based on deep convolutional neural network and spatio-temporal similarity

Existing learning-based super-resolution (SR) reconstruction algorithms are mainly designed for single image, which ignore the spatio-temporal relationship between video frames. Aiming at applying the advantages of learning-based algorithms to video SR field, a novel video SR reconstruction algorithm based on deep convolutional neural network (CNN) and spatio-temporal similarity (STCNN-SR) was proposed in this paper. It is a deep learning method for video SR reconstruction, which considers not only the mapping relationship among associated low-resolution (LR) and high-resolution (HR) image blocks, but also the spatio-temporal non-local complementary and redundant information between adjacent low-resolution video frames. The reconstruction speed can be improved obviously with the pre-trained end-to-end reconstructed coefficients. Moreover, the performance of video SR will be further improved by the optimization process with spatio-temporal similarity. Experimental results demonstrated that the proposed algorithm achieves a competitive SR quality on both subjective and objective evaluations, when compared to other state-of-the-art algorithms.

Small-world: Secure friend matching over physical world and social networks

The explosive growths of Social Networks and smart devices over the past decade have redefined the way people interact with their friends and build social connections with physical-proximate others. Unfortunately, increasing backbone network traffic and privacy concerns of the information leakage during the friend matching process limit the expansion of users’ friend circles. To address the problem, we propose Small-World, which aims to achieve secure friend matching over physical world and social networks simultaneously. Specifically, we design a physical proximity module, which is based on a novel weight-aware spatiotemporal similarity function with considering a set of factors such as hot zones for each user, the distribution of Point of Interests and users’ whereabouts. Then we build a Katz score-based social strength proximity module, which is a bidirectional and social strength-aware similarity function, to calculate the similarity between social network users. We design an El Gamal cryptosystem-based solution, termed Protocol I, to realize secure 2-hop social strength matching and further extend it to Protocol II, which aims to establish multi-hop (4-hop at most) social connection chain between two users. Finally, we design a weight assigning function based on the linear regression to evaluate the importance of the physical proximity module and it of social strength proximity module. Thorough security analysis and performance results indicate the effectiveness and efficiency of our proposed scheme.

Role of microglia in embryonic neurogenesis

Microglia begin colonizing the developing brain as early as embryonic day 9, prior to the emergence of neurons and other glia. Their ontogeny is also distinct from other central nervous system cells, as they derive from yolk sac hematopoietic progenitors and not neural progenitors. In this review, we feature these unique characteristics of microglia and assess the spatiotemporal similarities between microglia colonization of the central nervous system and embryonic neurogenesis. We also infer to existing evidence for microglia function from embryonic through to postnatal neurodevelopment to postulate roles for microglia in neurogenesis.

Causal Inference for Cross-Modal Action Selection: A Computational Study in a Decision Making Framework

Animals try to make sense of sensory information from multiple modalities by categorizing them into perceptions of individual or multiple external objects or internal concepts. For example, the brain constructs sensory, spatial representations of the locations of visual and auditory stimuli in the visual and auditory cortices based on retinal and cochlear stimulations. Currently, it is not known how the brain compares the temporal and spatial features of these sensory representations to decide whether they originate from the same or separate sources in space. Here, we propose a computational model of how the brain might solve such a task. We reduce the visual and auditory information to time-varying, finite-dimensional signals. We introduce controlled, leaky integrators as working memory that retains the sensory information for the limited time-course of task implementation. We propose our model within an evidence-based, decision-making framework, where the alternative plan units are saliency maps of space. A spatiotemporal similarity measure, computed directly from the unimodal signals, is suggested as the criterion to infer common or separate causes. We provide simulations that (1) validate our model against behavioral, experimental results in tasks where the participants were asked to report common or separate causes for cross-modal stimuli presented with arbitrary spatial and temporal disparities. (2) Predict the behavior in novel experiments where stimuli have different combinations of spatial, temporal, and reliability features. (3) Illustrate the dynamics of the proposed internal system. These results confirm our spatiotemporal similarity measure as a viable criterion for causal inference, and our decision-making framework as a viable mechanism for target selection, which may be used by the brain in cross-modal situations. Further, we suggest that a similar approach can be extended to other cognitive problems where working memory is a limiting factor, such as target selection among higher numbers of stimuli and selections among other modality combinations.

Regional assessment of pan-Pacific urban environments over 25 years using annual gap free Landsat data

Urbanization and the associated change in land cover has been intensifying across the globe in recent decades. Regional studies on the rate and amount of urban expansion are critical for understanding how patterns of change differ within and among cities with varying structure and development characteristics. Yet spatially consistent and timely information on urban development is difficult to access particularly across international jurisdictions. Remote sensing based technologies offer a unique perspective on urban land cover with the data offering significant potential to urban studies due to its consistent and ubiquitous nature. In this research we applied a pixel-based image composite technique to generate annual gap-free surface reflectance Landsat composites from 1984 to 2012 for 25 urban environments across 12 countries in the Pacific Rim. Using time series composites, spectral indices were calculated and compared using a hexagonal grid ring model to assess changes in vegetative and urban patterns. Trajectories were then clustered to further investigate the spatio-temporal dynamics and relationships among the 25 cities. Performance of the clustering analyses varied depended on the temporal and spatial metrics however overall clustering results indicated relatively strong spatio-temporal similarities among a number of key cities. Three pairs of cities—Melbourne and Sydney; Tianjin and Manila; and Singapore City and Kuala Lumpur were found to be highly similar in their urban and vegetation dynamics temporally and spatially. In contrast Vancouver and Las Vegas had no similar analogous. This work demonstrates the value of utilising annual Landsat time series composites for assessing urban vegetation and urban dynamics at regional scales and potential use in sustainable urban planning, resources allocation, and policy making.

Robust spatiotemporal stereo against image motion and temporal disparity variation

When there exists camera motion as well as scene motion, the position and disparity of a pixel in an image sequence temporally vary with time. Such image motion (IM) and temporal disparity variation (TDV) degrade the performance of spatiotemporal stereo matching. In this Letter, a robust spatiotemporal similarity measure that addresses IM and TDV is proposed. To this end, an irregular spatiotemporal window whose temporal windows are located by a TDV value and optical flows is designed. In addition, a spatiotemporal guided filter-based aggregation technique using temporal weights based on temporal proximity and flow reliability is presented. To handle a large number of labels effectively, a search range reduction method for finding a probable label set is presented. Experimental results show that the proposed method yields consistent and accurate disparity maps under IM and TDV.

Efficient Query Processing in 3D Motion Capture Gesture Databases

One of the most fundamental challenges when accessing gestural patterns in 3D motion capture databases is the definition of spatiotemporal similarity. While distance-based similarity models such as the Gesture Matching Distance on gesture signatures are able to leverage the spatial and temporal characteristics of gestural patterns, their applicability to large 3D motion capture databases is limited due to their high computational complexity. To this end, we present a lower bound approximation of the Gesture Matching Distance that can be utilized in an optimal multi-step query processing architecture in order to support efficient query processing. We investigate the performance in terms of accuracy and efficiency based on 3D motion capture databases and show that our approach is able to achieve an increase in efficiency of more than one order of magnitude with a negligible loss in accuracy. In addition, we discuss different applications in the digital humanities in order to highlight the significance of similarity search approaches in the research field of gestural pattern analysis.

Video super-resolution reconstruction based on correlation learning and spatio-temporal nonlocal similarity

A novel video super-resolution reconstruction algorithm based on correlation learning and spatio-temporal nonlocal similarity is proposed in this paper. Objective high-resolution (HR) estimates of low-resolution (LR) video frames can be obtained by learning LR-HR correlation mapping and fusing the spatio-temporal nonlocal similarity information between video frames. First, the LR-HR correlation mapping between LR and HR patches is established based on semi-coupled dictionary learning. With the aim of improving algorithm efficiency while guaranteeing super-resolution quality, LR-HR correlation mapping is performed only for the salient object region, and then an improved visual saliency-based nonlocal fuzzy registration scheme using the pseudo-Zernike moment feature and structural similarity is proposed for spatio-temporal similarity matching and fusion. Visual saliency and self-adaptive regional correlation evaluation strategies are used in spatio-temporal similarity matching to improve algorithm efficiency further. Experimental results demonstrate that the proposed algorithm achieves competitive super-resolution quality compared to other state-of-the-art algorithms in terms of both subjective and objective evaluations.

Framework for monitoring the conversion of cultivated land to construction land using SAR image time series

The land cover change from cultivated land to construction land is a world issue since the urbanization process is extensively studied around the world. Chengdu, China, is a representative urbanization area, where cloud cover is very high most of the time, restricting the use of visible and near-infrared satellite data. Here, we present a novel framework for land change monitoring based on synthetic aperture radar (SAR) time series, which comprises three key components: (1) construction of pixel-level SAR image time series; (2) spatio-temporal similarity analysis based on morphological-structural characteristics of time series; and (3) iterative binary partition mean square error model analysis to ascertain change nodes. Experimental results showed that the proposed framework could effectively extract the change nodes and change pixels, with correctness of 85.82% and completeness of 84.78%, outperforming the time-series-only (non-spatial) method, as well as traditional classification methods, and the same framework using shorter Landsat TM image time series.