A First Public Research Collection of High-Resolution Latent Fingerprint Time Series for Short- and Long-Term Print Age Estimation

2017 ◽  
Vol 12 (10) ◽  
pp. 2276-2291 ◽  
Author(s):  
Ronny Merkel ◽  
Jana Dittmann ◽  
Claus Vielhauer
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Age Estimation ◽  
Public Research ◽  
Latent Fingerprint ◽  
Research Collection ◽  
Short And Long Term

scholarly journals A Change-Driven Image Foveation Approach for Tracking Plant Phenology

2020 ◽  
Vol 12 (9) ◽  
pp. 1409
Author(s):  
Ewerton Silva ◽  
Ricardo da S. Torres ◽  
Bruna Alberton ◽  
Leonor Patricia C. Morellato ◽  
Thiago S. F. Silva
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Image Sequences ◽  
Plant Phenology ◽  
Memory Storage ◽  
High Resolution Images ◽  
The Creation ◽  
Computational Resources ◽  
Space Variant

One of the challenges in remote phenology studies lies in how to efficiently manage large volumes of data obtained as long-term sequences of high-resolution images. A promising approach is known as image foveation, which is able to reduce the computational resources used (i.e., memory storage) in several applications. In this paper, we propose an image foveation approach towards plant phenology tracking where relevant changes within an image time series guide the creation of foveal models used to resample unseen images. By doing so, images are taken to a space-variant domain where regions vary in resolution according to their contextual relevance for the application. We performed our validation on a dataset of vegetation image sequences previously used in plant phenology studies.

Multi-scale analysis of landslide occurrence and evolution using optical and radar time series

2020 ◽  
Author(s):  
Sigrid Roessner ◽  
Robert Behling ◽  
Mahdi Motagh ◽  
Hans Ulrich-wetzel
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
High Resolution ◽  
Intense Rainfall ◽  
Large Area ◽  
Landslide Occurrence ◽  
Multi Scale ◽  
Multi Temporal ◽  
Hazard And Risk

<p>Landslides represent a worldwide natural hazard and often occur as cascading effects related to triggering events, such as earthquakes and hydrometeorological extremes. Recent examples are the Kaikoura earthquake in New Zealand (November 2016), the Gorkha earthquake in Nepal (April/May 2015), and the Typhoon Morakot in Taiwan (August 2009) as well as less intense rainfall events persisting over unusually long periods of time as observed for Central Asia (spring 2017) and Iran (spring 2019). Each of these events has caused thousands of landslides that account substantially to the primary disaster’s impact. Moreover, their initial failure usually represents the onset of long-term progressing slope destabilization leading to multiple reactivations and thus to long-term increased hazard and risk. Therefore, regular systematic high-resolution monitoring of landslide prone regions is of key importance for characterization, understanding and modelling of spatiotemporal landslide evolution in the context of different triggering and predisposing settings. Because of the large extent of the affected areas of up to several ten thousands km<sup>2</sup>, the use of multi-temporal and multi-scale remote sensing methods is of key importance for large area process analysis. In this context, new opportunities have opened up with the increasing availability of satellite remote sensing data of suitable spatial and temporal resolution (Sentinels, Planet) as well as the advances in UAV based very high resolution monitoring and mapping.</p><p>During the last decade, we have been pursuing extensive methodological developments in remote sensing based time series analysis including optical and radar observations with the goal of performing large area and at the same time detailed spatiotemporal analysis of landslide prone regions. These developments include automated post-failure landslide detection and mapping as well as assessment of the kinematics of pre- and post-failure slope evolution.  Our combined optical and radar remote sensing approaches aim at an improved understanding of spatiotemporal dynamics and complexities related to evolution of landslide prone slopes at different spatial and temporal scales.  In this context, we additionally integrate UAV-based observation for deriving volumetric changes also related to globally available DEM products, such as SRTM and ALOS.  </p><p>We present results for selected settings comprising large area co-seismic landslide occurrence related to the Kaikoura 2016 and the Nepal 2015 earthquakes. For the latter one we also analyzed annual pre- and post-seismic monsoon related landslide activity contributing to a better understanding of the interplay between these main triggering factors. Moreover, we report on ten years of large area systematic landslide monitoring in Southern Kyrgyzstan resulting in a multi-temporal regional landslide inventory of so far unprecedented spatiotemporal detail and completeness forming the basis for further analysis of the obtained landslide concentration patterns. We also present first results of our analysis of landslides triggered by intense rainfall and flood events in spring of 2019 in the North of Iran. We conclude that in all cases, the obtained results are crucial for improved landslide prediction and reduction of future landslide impact. Thus, our methodological developments represent an important contribution towards improved hazard and risk assessment as well as rapid mapping and early warning</p>

scholarly journals Characterizing scaling laws in gut microbial dynamics from time series data: caution is warranted

2021 ◽  
Author(s):  
Xu-Wen Wang ◽  
Yang-Yu Liu
Keyword(s):  
Time Series ◽  
Gut Microbiota ◽  
Scaling Laws ◽  
Time Series Data ◽  
Series Data ◽  
Temporal Variance ◽  
Short And Long Term ◽  
Human And Mouse ◽  
Mouse Gut Microbiota

AbstractMany studies have revealed that both host and environmental factors can impact the gut microbial compositions, implying that the gut microbiota is considerably dynamic1–5. In their Article, Ji et al.6 performed comprehensive analysis of multiple high-resolution time series data of human and mouse gut microbiota. They found that both human and mouse gut microbiota dynamics can be characterized by several robust scaling laws describing short- and long-term changes in gut microbiota abundances, distributions of species residence and return times, and the correlation between the mean and the temporal variance of species abundances. They claimed that those scaling laws characterize both short- and long-term dynamics of gut microbiota. However, we are concerned that their interpretation is quite misleading, because all the scaling laws can be reproduced by the shuffled time series with completely randomized time stamps of the microbiome samples.

scholarly journals Air pollution, SARS-CoV-2 transmission, and COVID-19 outcomes: A state-of-the-science review of a rapidly evolving research area

2020 ◽  
Author(s):  
Anushka Bhaskar ◽  
Jay Chandra ◽  
Danielle Braun ◽  
Jacqueline Cellini ◽  
Francesca Dominici
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Health Outcomes ◽  
Study Design ◽  
Air Pollutant ◽  
Short Term ◽  
Cross Sectional ◽  
Short And Long Term ◽  
Long Term Studies

Background: As the coronavirus pandemic rages on, 692,000 (August 7, 2020) human lives and counting have been lost worldwide to COVID-19. Understanding the relationship between short- and long-term exposure to air pollution and adverse COVID-19 health outcomes is crucial for developing solutions to this global crisis. Objectives: To conduct a scoping review of epidemiologic research on the link between short- and long-term exposure to air pollution and COVID-19 health outcomes. Method: We searched PubMed, Web of Science, Embase, Cochrane, MedRxiv, and BioRxiv for preliminary epidemiological studies of the association between air pollution and COVID-19 health outcomes. 28 papers were finally selected after applying our inclusion/exclusion criteria; we categorized these studies as long-term studies, short-term time-series studies, or short-term cross-sectional studies. One study included both short-term time-series and a cross-sectional study design. Results: 27 studies of the 28 reported evidence of statistically significant positive associations between air pollutant exposure and adverse COVID-19 health outcomes; 11 of 12 long-term studies and all 16 short-term studies reported statistically significant positive associations. The 28 identified studies included various confounders, spatial and temporal resolutions of pollution concentrations, and COVID-19 health outcomes. Discussion: We discuss methodological challenges and highlight additional research areas based on our findings. Challenges include data quality issues, ecological study design limitations, improved adjustment for confounders, exposure errors related to spatial resolution, geographic variability in testing, mitigation measures and pandemic stage, clustering of health outcomes, and a lack of publicly available data and code.

scholarly journals Retrieval of aerosol optical depth over land surfaces from AVHRR data

2013 ◽  
Vol 6 (1) ◽  
pp. 2227-2251 ◽  
Author(s):  
L. Mei ◽  
Y. Xue ◽  
A. A. Kokhanovsky ◽  
W. von Hoyningen-Huene ◽  
G. de Leeuw ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Surface Reflectance ◽  
Aerosol Retrieval ◽  
Avhrr Data ◽  
Land Surfaces ◽  
Very High

Abstract. The Advanced Very High Resolution Radiometer (AVHRR) radiance data provide a global, long-term, consistent time series having high spectral and spatial resolution and thus being valuable for the retrieval of surface spectral reflectance, albedo and surface temperature. Long term time series of such data products are necessary for studies addressing climate change, sea ice distribution and movement, and ice sheet coastal configuration. These data have also been used to retrieve aerosol properties over ocean and land surfaces. However, the retrieval of aerosol over land and land surface albedo are challenging because of the information content of the measurement is limited and the inversion of these data products being ill defined. Solving the radiative transfer equations requires additional information and knowledge to reduce the number of unknowns. In this contribution we utilise an empirical linear relationship between the surface reflectances in the AVHRR channels at wavelengths of 3.75 μm and 2.1 μm, which has been identified in Moderate Resolution Imaging Spectroradiometer (MODIS) data. Next, following the MODIS dark target approach, the surface reflectance at 0.64 μm was obtained. The comparison of the estimated surface reflectance at 0.64 μm with MODIS reflectance products (MOD09) shows a strong correlation (R = 0.7835). Once this was established, the MODIS "dark-target" aerosol retrieval method was adapted to Advanced Very High Resolution Radiometer (AVHRR) data. A simplified Look-Up Table (LUT) method, adopted from Bremen AErosol Retrieval (BAER) algorithm, was used in the retrieval. The Aerosol Optical Depth (AOD) values retrieved from AVHRR with this method compare favourably with ground-based measurements, with a correlation coefficient R = 0.861 and Root Mean Square Error (RMSE) = 0.17. This method can be easily applied to other satellite instruments which do not have a 2.1 μm channel, such as those currently planned to geostationary satellites.

Continuous Dynamical Combination of Short and Long-Term Forecasts for Nonstationary Time Series

2014 ◽  
Vol 25 (1) ◽  
pp. 241-246 ◽  
Author(s):  
Domingos Savio Pereira Salazar ◽  
Paulo Jorge Leitao Adeodato ◽  
Adrian Lucena Arnaud
Keyword(s):  
Time Series ◽  
Nonstationary Time Series ◽  
Short And Long Term

scholarly journals INTRODUCTION TO TIME-VARYING MODELING WITH MACROECONOMIC AND FINANCIAL DATA

2012 ◽  
Vol 16 (S2) ◽  
pp. 167-175 ◽  
Author(s):  
Fredj Jawadi
Keyword(s):  
Time Series ◽  
Time Variation ◽  
Moving Average ◽  
Statistical Properties ◽  
Financial Data ◽  
Time Varying ◽  
Arch Model ◽  
Financial Variables ◽  
Short And Long Term

The dynamics of macroeconomic and financial series has evolved swiftly and asymmetrically since the end of the 1970s, and their statistical properties have also changed over time, suggesting complex relationships between economic and financial variables. The transformations can be explained by considerable changes in householder's behavior, market structures, and economic systems and by the alternation of exogenous shocks and financial crises that have affected the economic cycle, with significant evidence of time variation in the major economic variables. Hence, there is a need for new econometric protocols to take such changes into consideration. The introduction of ARMA (autoregressive moving average models) by Box and Jenkins (1970) led to the development of time-series econometrics, which had a major impact on the conceptual analysis of economic and financial data. This type of modeling offered a transition from a static setup to a new modeling process that reproduces the time-varying features of macroeconomic and financial series. However, the ARMA modeling system retains the constancy of the first and second moments, limits the phases of a cycle to symmetrical instances, and only reproduces the dynamics of stationary variables. It thus fails to adequately reproduce the nonstationary relationships between major economic and financial variables. Abrupt changes in economies and financial systems have given evidence of nonstationary series whose statistical properties are also time-varying, making it necessary to develop new econometric tools to capture the time variation of economic and financial series in the mean and in the variance, and to apprehend their dynamics in the short and long term. Among the most important and influential studies in the 1980s' econometrics literature were therefore those that dealt with the introduction of the ARCH (autoregressive conditional heteroskedasticity) model by Engle (1982) and the cointegration theory by Engle and Granger (1987). The ARCH model, which focuses on the time-varying features of volatility structure, was a major breakthrough, as it highlighted the importance of the second moment of time series, while the cointegration framework enabled the short- and long-term dynamics of nonstationary variables to be modeled.

scholarly journals Modelling Short- and Long-Term Dependencies of Clustered High-Threshold Exceedances in Significant Wave Heights

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2817
Author(s):  
Pushpa Dissanayake ◽  
Teresa Flock ◽  
Johanna Meier ◽  
Philipp Sibbertsen
Keyword(s):  
Time Series ◽  
Long Range ◽  
High Threshold ◽  
Peaks Over Threshold ◽  
Significant Wave ◽  
Wave Heights ◽  
Short And Long Term ◽  
Significant Wave Heights ◽  
Threshold Exceedances

The peaks-over-threshold (POT) method has a long tradition in modelling extremes in environmental variables. However, it has originally been introduced under the assumption of independently and identically distributed (iid) data. Since environmental data often exhibits a time series structure, this assumption is likely to be violated due to short- and long-term dependencies in practical settings, leading to clustering of high-threshold exceedances. In this paper, we first review popular approaches that either focus on modelling short- or long-range dynamics explicitly. In particular, we consider conditional POT variants and the Mittag–Leffler distribution modelling waiting times between exceedances. Further, we propose a new two-step approach capturing both short- and long-range correlations simultaneously. We suggest the autoregressive fractionally integrated moving average peaks-over-threshold (ARFIMA-POT) approach, which in a first step fits an ARFIMA model to the original series and then in a second step utilises a classical POT model for the residuals. Applying these models to an oceanographic time series of significant wave heights measured on the Sefton coast (UK), we find that neither solely modelling short- nor long-range dependencies satisfactorily explains the clustering of extremes. The ARFIMA-POT approach, however, provides a significant improvement in terms of model fit, underlining the need for models that jointly incorporate short- and long-range dependence to address extremal clustering, and their theoretical justification.

scholarly journals Artificial mimicry of seasonal transcriptome dynamics in Arabidopsis thaliana reveals short- and long-term responses to environmental conditions

2021 ◽  
Author(s):  
Yuko Kurita ◽  
Hironori Takimoto ◽  
Mari Kamitani ◽  
Yoichi Hashida ◽  
Makoto Kashima ◽  
...  
Keyword(s):  
Time Series ◽  
Arabidopsis Thaliana ◽  
Wild Population ◽  
Low Cost ◽  
Smart Growth ◽  
Experimental System ◽  
Arabidopsis Halleri ◽  
Short Term ◽  
Short And Long Term

Plants must respond to various environmental factors that change seasonally. In a previous study, seasonally oscillating genes were identified by a massive time-series transcriptome analysis in a wild population of Arabidopsis halleri ssp. gemmifera, a sister species of Arabidopsis thaliana. To analyze the function of these seasonally oscillating genes, we established an experimental system to mimic seasonal expression trends using A. thaliana. Arabidopsis thaliana plants were cultured under conditions that mimicked average monthly temperatures and daylengths in a "smart growth chamber mini," a hand-made low-cost small chamber. Under different short-term incubations, the seasonal trends of 1627 seasonally oscillating genes were mimicked. These seasonally oscillating genes had varying temporal responsiveness (constant, transient, and incremental). Our findings suggest that plants perceive and integrate information about environmental stimuli in the field by combining seasonally oscillating genes with temporal responsiveness.

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