spatio-temporal data analysis

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A Bayesian Approach for the Multifractal Analysis of Spatio-Temporal Data

A Bayesian Approach for the Multifractal Analysis of Spatio-Temporal Data

Abstract—Multifractal (MF) analysis enables the theoretical study of scale invariance models and their practical assessment via wavelet leaders. Yet, the accurate estimation of MF param- eters remains a challenging task. For a range of applications, notably biomedical, the performance can potentially be improved by taking advantage of the multivariate nature of data. However, this has barely been considered in the context of MF analysis. This paper proposes a Bayesian model that enables the joint estimation of MF parameters for multivariate time series. It builds on a recently introduced statistical model for leaders and is formulated using a 3D gamma Markov random field joint prior for the MF parameters of the voxels of spatio-temporal data, represented as a multivariate time series, that counteracts the statistical variability induced by small sample size. Numer- ical simulations indicate that the proposed Bayesian estimator significantly outperforms current state-of-the-art algorithms.
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Spatio-Temporal Shape Analysis of Cross-Sectional Data for Detection of Early Changes in Neurodegenerative Disease

Spatio-Temporal Shape Analysis of Cross-Sectional Data for Detection of Early Changes in Neurodegenerative Disease

6. Datar, M., Muralidharan, P., Kumar, A., Gouttard, S., Piven, J., Gerig, G., Whitaker, R., Fletcher, P.T.: Mixed-E↵ects Shape Models for Estimating Lon- gitudinal Changes in Anatomy. In: Hutchison, D., Kanade, T., Kittler, J., Klein- berg, J.M., Mattern, F., Mitchell, J.C., Naor, M., Nierstrasz, O., Pandu Rangan, C., Ste↵en, B., Sudan, M., Terzopoulos, D., Tygar, D., Vardi, M.Y., Weikum, G., Durrleman, S., Fletcher, T., Gerig, G., Niethammer, M. (eds.) Spatio-temporal Im- age Analysis for Longitudinal and Time-Series Image Data, vol. 7570, pp. 76–87. Springer Berlin Heidelberg, Berlin, Heidelberg (2012)
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A Bayesian Approach for the Multifractal Analysis of Spatio-Temporal Data

A Bayesian Approach for the Multifractal Analysis of Spatio-Temporal Data

patrice.abry@ens-lyon.fr Abstract—Multifractal (MF) analysis enables the theoretical study of scale invariance models and their practical assessment via wavelet leaders. Yet, the accurate estimation of MF param- eters remains a challenging task. For a range of applications, notably biomedical, the performance can potentially be improved by taking advantage of the multivariate nature of data. However, this has barely been considered in the context of MF analysis. This paper proposes a Bayesian model that enables the joint estimation of MF parameters for multivariate time series. It builds on a recently introduced statistical model for leaders and is formulated using a 3D gamma Markov random field joint prior for the MF parameters of the voxels of spatio-temporal data, represented as a multivariate time series, that counteracts the statistical variability induced by small sample size. Numer- ical simulations indicate that the proposed Bayesian estimator significantly outperforms current state-of-the-art algorithms.
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Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data

Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data

Figure 8: Ratio between the model prediction at time tp and the prediction at t0 for the three imaging modalities. The time-scale was re-scaled to the arbitrary range [0, 1]. coefficient was never significant. Pearson correlation coefficients for ADAS11, FAQ and MMSE were respectively of 0.49, 0.41, and −0.45, with corresponding p-values p < 0.01. The box-plot of Figure 12 shows the time-shift distribution across clinical groups. We observe an increase of the estimated time-shift when going from healthy to pathological stages. The high uncertainty associated to the MCI group is due to the broad definition of this clinical category, which includes subjects not necessarily affected by dementia. We note that MCI subjects subsequently converted to AD (MCI converter) exhibit higher time-shift than the clinically stable MCI group, highlighting the ability of the model to differentiate between conversion status. A similar distinction can be noticed between NL and NL converter groups. We found significant differences between median time-shift for NL-NL converter, MCI-MCI converter and MCI converter-AD (comparisons p < 0.01, Figure 12). It is also important to recall that this result is obtained from the analysis of a single scan per imaging modality and ADAS13 score for each patient.
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Thymeflow, A Personal Knowledge Base with Spatio-Temporal Data

Thymeflow, A Personal Knowledge Base with Spatio-Temporal Data

Keywords personal information; data integration; querying; open-source 1. INTRODUCTION Today, typical Internet users have their data spread over several devices and services. This includes emails, contact lists, calendars, location histories, and many other types of data. However, commercial systems often function as data traps, where it is easy to check in information and difficult to query it. This problem becomes all the more important as more and more of our lives happens in the digital sphere. With this paper, we propose to demonstrate a fully functional personal knowledge management system, called Thymeflow. Our system integrates personal information from different sources into a single knowledge base (KB). The system runs locally on the users’ machine, and thus gives them complete control over their data. Thymeflow replicates data from outside services (such as email, calendar, contacts, location services, etc.), and thus acts as a digital home for personal data. This provides users with a high-level global view of that data, which they can use for querying and analysis.
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An Iconography-Based Modeling Approach for the Spatio-Temporal Analysis of Architectural Heritage

An Iconography-Based Modeling Approach for the Spatio-Temporal Analysis of Architectural Heritage

Today modeling software enables users to obtain particularly rich and complex geometries with a high level of detail. Moreover, according to [1], computer graphics offers a vast range of visualizations (wireframe, transparency, broken lines or various rending modes) which leads us to think that architectures are always described in a correct, objective and complete way. In the panorama of current researches, various information representation approaches are proposed. In some works [2], historical reconstructions are geographically well integrated; however the result is a set of photorealistic images showing an historical site that does not exist anymore. The historical state is so detailed that the public is led to believe that the site was very similar to the restituted one. In other works, hypothetical parts and historical states are displayed by color coding [3], by transparency [4], wireframe or broken lines [1], as the goal is to distinguish the certainty levels. However, uncertainty interpretation is limited to the 3D scene: parts are shown with different transparency levels but data cannot be fully manipulated and information cannot be fully extracted [5]. Other researches propose mixed approaches combining the 3D scene with temporal diagrams [6]. Uncertainties and historical states are clearer, however lack of information and building transformations are not taken into account.
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Learning spatio-temporal trajectories from manifold-valued longitudinal data

Learning spatio-temporal trajectories from manifold-valued longitudinal data

In the spirit of Independent Component Analysis, the space shift 𝒘 𝑖 appears as a linear combination of 𝑁 𝑠 independent components, namely the columns of the matrix 𝐀. Aim : we want to analyze the temporal progression of a family of 𝑁 biomarkers.  We assume that the measurements of each biomarker belong to a one-dimensional Riemannian manifold 𝐼, geodesically complete and included in 𝐑. As a consequence, 𝑀 is a product of one- dimensional manifolds : 𝑀 = 𝐼 𝑁 = 𝐼 × 𝐼 × ⋯ × 𝐼.

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Spatio-temporal Genetic Structuring of Leishmania major in Tunisia by Microsatellite Analysis

Spatio-temporal Genetic Structuring of Leishmania major in Tunisia by Microsatellite Analysis

and Pakistan. (XLSX) Acknowledgments The authors want to thank the Regional Health Directorates of Sidi Bouzid, Kairouan, and Gafsa (Tunisia), the “Institut Pasteur Tunis” and the “Institut de Recherche pour le Développe- ment ” (IRD, Montpellier, France), the “Centre National de la Recherche Scientifique” (CNRS, France) for this cooperative and collaborative project. We acknowledge the field staff of the Department of Medical Biology, Institut Pasteur Tunis, for strain collections and the cryobank management; Adel Gharbi for ensuring strain and patients’ data collection in accordance with the current Good Clinical Practice guidelines. Many thanks also to Dr. Francine Pratlong and Patrick Lami for providing samples from the French National Reference Center of Leishmania (CNRL, Montpellier, France) and Luc Abate (IRD, Montpellier, France) for help with the geno- typing analysis. We are grateful to Pr. Jonathan K. Pritchard (Department of Biology and Genetics, Stanford University and Howard Hughes Medical Institute), Dr. Vikram E. Chhatre (University of Maryland, Center for Environmental Sciences), Dr. Dent Earl (Department of Biomolecular Engineering, Jack Baskin School of Engineering, University of California, Santa Cruz) for helpful comments and discussion. Great acknowledgments go to Pr. Koussay Dellagi from the Centre de Recherche et de Veille sur les Maladies Emergentes dans l ’Océan Indien (CRVOI) for his useful comments on the manuscript. We thank Elisabetta Andermarcher for assistance in preparing and editing the manuscript.
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Studying Media Events through Spatio-Temporal Statistical Analysis

Studying Media Events through Spatio-Temporal Statistical Analysis

CHAPTER 5. CASE STUDY 23 A first model in the most basic form, with one state sequence and the same success probabilities π i for state i for all 65 feeds, could not be fitted successfully due to problems with the numerical optimisation routine: No suitable initial values could be found. This is not astonishing as such a model is unlikely to describe the data properly, assuming ba- sically no heterogeneity between feeds. Consequently, we included feed-specific covariate information in the observation model to account for inter-feed differences. A readily avail- able covariate, which not only varies across feeds, but also in time, is the total number of daily publications (Model I). This choice is backed up by a preliminary analysis from a generalised linear model (not taking into account the temporal dependence in the obser- vations) which retained the total number of items as a significant explanatory variable. A HMM with this covariate was fitted for 1 to 6 states. The model with 1 state technically corresponds to independent realisations (no temporal correlation) of observations from a product Binomial with paramter π k . It was included for completeness. Both AIC and BIC favour the model with 4 states (see Table 5.1). In the following, we present the results for this model. The point estimate of the transition matrix is
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R&D policy regimes in France: New evidence from a spatio-temporal analysis

R&D policy regimes in France: New evidence from a spatio-temporal analysis

Thus, the objective of this paper is to implement a spatial model using regionally aggregated data in order to investigate the effect of the French policy mix in favor of private R&D. Assembling fiscal and survey data, we gathered information concerning the amount of total R&D tax credits and regional, national and European subsidies received by firms conducting R&D activities in each French metropolitan NUTS3 region and their total investment in R&D over the period 2001-2011. To run our analysis, we first develop a simple theoretical model based on Howe and McFetridge (1976) that provides one explanation for the ambiguous empirical results obtained concerning the effect of R&D policies. Indeed, depending on the key parameter values, crowding-out as well as crowding-in effects can emerge. Also, this framework can be easily extended to spatial interactions and will provide a basis for our empirical estimates. More precisely, we estimate a spatial Durbin model with regimes and fixed effects. This type of spatial model allows us to take into account not only the spatial dependency between units but also the potential structural change due to changes in policies during the considered period. This paper offers three main contributions to the literature on the geography of innovation and evaluation of R&D policies: (i) it investigates spatial interdependencies and in particular the possible existence of a yardstick competition between NUTS3 regions for R&D investment in France that can hamper the global effects of policy instruments; (ii) it simultaneously considers different components of the R&D policy mix–regional, national and European subsidies and allows interpretation of the results in terms of total, direct (internal to each region) and indirect (external) marginal effects of each instrument on private R&D spending;(iii) it also measures the potential structural change in the behavior of firms that can be related to the mid-2000’s strong changes in French R&D policies.
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Poverty and Female Homicide in Mexican Municipalities: A Bayesian Spatio-Temporal Analysis

Poverty and Female Homicide in Mexican Municipalities: A Bayesian Spatio-Temporal Analysis

precipitating interactions characterize the etiology of specific homicide types, and serve as clues to whether particular homicide types are susceptible to processes of contagion (Zeoli et al 2015). Data This study uses poverty data produced by the Consejo Nacional de Evaluacion de la Politica de Desarrollo Social (CONEVAL) for each Mexican municipality for the years 1990, 2000, and 2010. The methodology followed by CONEVAL consists of estimating poverty based on income levels, which in turn defines three alternative measures of poverty: food-based poverty, capabilities-based poverty, and assets-based poverty (which are equivalent to extreme poverty, poverty, and moderate poverty). A household is considered food poor if its members’ income falls below the lowest income necessary to afford a minimum basket of food. A household is considered to be in capabilities-based poverty if its members cannot afford to cover their basic expenses of food, health, and education, according to an officially defined basket. Finally, a household is considered to be in assets-based poverty if its members cannot cover their expenses of food, health, education, dressing, home, and public transportation (Rodriguez-Oreggia et al. 2013).
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Modeling spatio-temporal Variation of Algal bloom using MODIS inland Waters Data processing.

Modeling spatio-temporal Variation of Algal bloom using MODIS inland Waters Data processing.

• To develop a water bodies (inland, coastal, and open ocean) cloud masking based on a linear discriminant analysis algorithm using MODIS-D-250. • To establish a regional portrayal of the harmful algal blooms (HABs) occurrence on Southern Quebec using a geospatial database including the phenology features of HABs (e.g. beginning, duration, intensity).

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Spatio-temporal analysis of malaria within a transmission season in Bandiagara, Mali

Spatio-temporal analysis of malaria within a transmission season in Bandiagara, Mali

While seldom prioritized in the planning of malaria control by national programmes, the understanding of the microepidemiology of malaria is important to the de- sign of effective small-area interventions [3,18], particu- larly in areas of unstable or very low transmission. To assess space-time local heterogeneity of disease, statistics that detect the presence of significant small-area disease clusters are often useful [2,7,25]. The space-time cluster- ing of malaria has also been described, mainly in moder- ate to high transmission settings [2,13,26-30]. A few studies showed a difference of malaria risk at the re- gional or local level [27,31]. A precise knowledge of the geographic zones at risk, the levels of risk, the various risk factors, and the exposed populations, is required particularly in sites where malaria vaccines are tested. In order to assess space and time distribution of malaria disease in children in Bandiagara, Mali, within a trans- mission season, the data from a malaria incidence study have been used.
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Spatio-Temporal data mining: From big data to patterns

Spatio-Temporal data mining: From big data to patterns

the development of traffic planning in large cities according to vehicle- flows. Other application do- mains include socio-economic geography, sports (e.g. football players), fishing control and weather forecast- (e.g. hurricanes). In most of these appli- cations, the number of paths is high. One of the objectives of trajectory analysis is to find the most relevant paths according to the targeted problem (e.g. the most frequent, the most unexpected, peri- odic, etc.). Several approaches have been recently proposed in the literature, for instance (Orakzai et al., 2015).

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Spatio-Temporal Predictability of Cellular Data Traffic

Spatio-Temporal Predictability of Cellular Data Traffic

2 Related work Since the turn of the millennium, traffic predictability has attracted attention in the wired net- working community [14, 19]. As a consequence, the literature on the study of cellular network traffic has grown dramatically [1]. A number of studies have attempted to understand cellular data traffic. The authors in [16] modeled the volume distribution of Internet data traffic towards an improved traffic volume prediction. Oliveira et al. [17] proposed a measurement-driven model of mobile data traffic and a synthetic mobile data traffic generator. Both these studies do not consider the influence of subscribers’ mobility on their mobile service consumption. The relation between content consumption and mobility properties is considered in studies that focus on ap- plication interests [20], data traffic dynamics [15] and service usages [18]. However, none of these works provides a complete analysis of the predictability of the mobile data traffic consumption of individual subscribers, with respect to both time and space dimensions – as done in this work. Recently, two studies contributed to our understanding of the predictability of cellular data traffic. Zhou et al. [10] analyzed the predictability of voice, text, and data traffic in cellular networks. Li et al. [11] focused on the traffic predictability in Cloud radio access networks (C- RANs) and proposed future potential of software-defined C-RAN paradigms that benefit from the traffic prediction. These two works have an aggregated perspective, only distinguishing all traffic served by each base station. Instead, we analyze the traffic predictability from the viewpoint of individual users, as motivated in the introduction.
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Spatio-temporal relationships in a primitive space: an attempt to simplify spatio-temporal analysis

Spatio-temporal relationships in a primitive space: an attempt to simplify spatio-temporal analysis

P.Hallot@ulg.ac.be I NTRODUCTION Nowadays, huge amount of dynamic spatial data is available notably due to the improvement of data acquisition techniques (e.g. on-board GPS). This provides basic information allowing performing complex spatio-temporal analyses such as, movement description, region evolution, trajectory calcu- lus,… However, considering time in spatial analyses increases rapidly their complexity (Renz and Nebel, 1999). We are still far from standard spatio- temporal functionality. An answer to this assessment can be a generalisa- tion of spatio-temporal relationships. Considering a higher level of rela- tionships abstraction should reduce spatio-temporal analysis complexity. However, such generalised model should preserve enough spatio-temporal signification.
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Spatio-temporal registration of electro-anatomical mappings with functional data for CRT optimization

Spatio-temporal registration of electro-anatomical mappings with functional data for CRT optimization

Our goal is to better plan the placement of CRT leads using anatomical, functional and electrical data ac- quired with cardiac Computed Tomography (CT) imaging, Speckle Tracking Echocadiography (STE) and Electro- Anatomical Maps (EAM) respectively 1 . In previous works, we proposed to solve geometrical registration prob- lems between CT and EAM data by a semi-interactive method [3] rather than mainly used simultaneous acqui- sitions [4] and between CT and STE data by geometrical method using the entire cardiac cycle [5] rather than land- mark methods [6]. These two methods give a common 3D space for the three modalities.
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Spatio-temporal compressed quantitative acoustic microscopy

Spatio-temporal compressed quantitative acoustic microscopy

Quantitative acoustic microscopy (QAM) is an ultrasound imaging modality using very high frequency ultrasound to form 2D maps of acoustic properties of soft tissues at mi- croscopic scales [1], [2]. For our QAM system, thin ex vivo samples are affixed to a microscopy glass slides and are raster-scanned (spatial step size of 2 µm) using a 250 MHz transducer resulting in a 3D RF data cube. Each RF signal is processed to obtain, for each spatial location, acoustic parameters, e.g., speed of sound (c). The scanning time is dependent on the sample size and can range from less than one minute to possibly tens of minutes. In order to prevent changes to the sensitive thin sectioned tissue during scanning, reducing scanning time is an important practical issue. In this regard, our previous studies were devoted to demonstrating: i) spatially under sampled measurements, following a spiral pattern combined with image reconstruction based on approx- imate message passing (AMP), allow decreasing the number of acquired RF signals by 40% without degrading the QAM image quality [3], ii) because QAM RF signals at a given location follow a parametric form with a limited number of degrees of freedom, each RF signal can be sampled (and
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Spatio-Temporal Saliency Based on RARE Model

Spatio-Temporal Saliency Based on RARE Model

Index Terms— Visual attention, Saliency, Rarity Mechanism, Optical Flow 1. INTRODUCTION The aim of visual saliency models is to automatically predict human attention. The term attention refers to the process that allows one to focus on some stimuli at the expense of others and has been introduced in [1], [2]. Human attention mainly consists of bottom-up and top- down processes. Bottom-up attention uses features extracted from the signal to find the most salient objects. Top-down attention uses a priori knowledge about the scene or task- oriented knowledge in order to modify the bottom-up saliency. This domain is a very active area due to several important applications such as gaze prediction [3], content aware compression [4], video retargeting [5], and video summary [6]. The general idea of saliency models is that rare, novel or surprising information is salient. The objective of those models is to identify unusual features in a given spatio-temporal context like in [7], [8], [9], [10], [11], [12], [13], [14], [15], [16]. In this paper, the models [12], [13], [14], [15], [16] has been chosen for the comparison.
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Spatio-temporal Attention Mechanisms for Activity Recognition

Spatio-temporal Attention Mechanisms for Activity Recognition

Recent inflated convolutions [ 3 ] have shown significant improvement compared to RNNs. The non-local behavior of non-local block on top of I3D [ 85 ], along space-time in Smarthome is not view-invariant because its attention mechanism relies on appearance. On the contrary, our proposed attention mechanisms are guided by 3D pose information, which is view-invariant. The significant improvement of our attention mechanisms (P- I3D, Separable STA and VPN) on cross-view protocols shows its view-invariant property compared to existing methods. In fig. 6.1 we provide some visual examples in which Sep- arable STA outperforms I3D (without attention). We also observe that, VPN significantly improves the state-of-the-art results on Smarthome. This is largely due to better under- standing of the fine-grained actions like cut bread, cooking.stirring and so on, by VPN. We also note that the Temporal Model in this dataset sometimes outperforms the video back- bone itself, for instance in CS protocol using Temporal Model with P-I3D and Separable STA as video backbones. This shows the importance of modeling temporal information in this dataset. As a consequence, the Global Model, especially VPN, shows significant improvement compared to all our results on Smarthome. Our results also substantiates the fact that how important is this pose driven attention mechanism for real-world action recognition. Even today when we are dealing with noisy 3D poses obtained from pose estimation algorithms [ 134 ] in the wild, our attention mechanisms recognize the actions. It is to be noted that AssembleNet++ [ 191 ] outperforms our models due to the additional use of optical flow as well as object cues. Moreover, it is a NAS architecture which leads to low generalization power of the framework.
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