Time-varying Data Research Articles

Effect of time-variant rainfall on landslide susceptibility: A case study in Quang Ngai Province, Vietnam

Rainfall is a triggering factor that causes landslides, especially in the regions where landslides often occur after consecutive days of heavy rainfall. Most previous studies only used a specific rainfall map for landslide susceptibility assessment. However, this approach was unreasonable because rainfall is a time-variant data. This study uses the time series data of 1-day, 3-day, 5-day, and 7-day maximum precipitation from 2016 to 2020 in the mountainous area of Quang Ngai province for landslide susceptibility assessment. These data and other influencing factors were used to develop landslide spatial prediction models using the Extreme Gradient Boosting method. The prediction model's performance was assessed using the statistical index and receiver operating characteristic curve methods. The testing results of 4 cases using consecutive days of maximum rainfall data demonstrated excellent performance. Of these, the model with a 3-day maximum rainfall with ACC = 0.813, kappa = 0.625, SST = 0.872, SPF = 0.754, and AUC = 0.895 had the best performance. In addition, these results were compared to the previous approach that used average annual rainfall. The validation result indicates that the cases using a time series of maximum precipitation (with AUC of approximately 0.9) outperform the cases with average annual rainfall (AUC=0.838). Finally, the model using 3-day maximum rainfall is then used for landslide spatial prediction mapping. These maps provide spatial prediction and assess landslide susceptibility corresponding to rainfall frequencies.

Extracting Dynamic Information of Temporal Clinical Data to Predict the Outcome in Critically Ill Patients.

Outcome prediction is essential for the administration and treatment of critically ill patients. For those patients, clinical measurements are continuously monitored and the time-varying data contains rich information for assessing the patients' status. However, it is unclear how to capture the dynamic information effectively. In this work, multiple feature extraction methods, i.e. statistical feature classification methods and temporal modeling methods, such as recurrent neural network (RNN), were analyzed on a critical illness dataset with 18415 cases. The experimental results show when the dimension increases from 10 to 50, the RNN algorithm is gradually superior to the statistical feature classification methods with simple logic. The RNN model achieves the largest AUC value of 0.8463. Therefore, the temporal modeling methods are promising to capture temporal features which are predictive of the patients' outcome and can be extended in more clinical applications.

“Right-Peopling” the State: Nationalism, Historical Legacies, and Ethnic Cleansing in Europe, 1886-2020

Many European nation-states were historically homogenized through violent ethnic cleansing. Despite its historical importance, we lack systematic evidence of the conditions under which groups where targeted with cleansing and how it impacted states’ ethnic demography. Rising nationalism in the nineteenth century threatened multi-ethnic states with “right-sizing” through secessionism and irredentism. States therefore frequently turned to brutal “right-peopling”, in particular where cross-border minorities and those with a history of political independence increased the risk of territorial losses. We test this argument with new spatial, time-variant data on ethnic geography and ethnic cleansing from 1886 to the present. We find that minorities that politically dominated another state and those that have lost political independence were most at risk of ethnic cleansing, especially in times of interstate war. At the macro-level, our results show that ethnic cleansing increased European states’ ethnic homogeneity almost as much as border change. Both produced today’s nation-states by aligning states and ethnic nations.

Observer-based HOFA predictive cooperative control for networked multi-agent systems under time-variant communication constraints

This research focuses on a cooperative control problem for networked multi-agent systems (NMASs) under time-variant communication constraints (containing time-variant communication delays and time-variant data losses) in the forward and feedback channels. From the perspective of high-order fully actuated (HOFA) system theory, a HOFA system model is adopted to describe the NMAS, which is called the networked HOFA multi-agent system (NHOFAMAS). Because of complicated working scenarios over the network, the states of NMASs are immeasurable and the communication constraints are always present, such that an observer-based HOFA predictive control (OB-HOFAPC) method is designed to implement the cooperative control when existing the immeasurable states and time-variant communication constraints. In this method, a HOFA observer is established to estimate the immeasurable states for constructing a consensus control protocol. Then, an incremental prediction model (IPM) in a HOFA form is developed via a Diophantine equation to take the place of a reduced-order prediction model. Through this IPM, multi-step output ahead predictions are derived to optimize the cooperative control performance and compensate for time-variant communication constraints in real-time. The depth discussion gives a sufficient and necessary criterion to analyze the simultaneous consensus and stability for closed-loop NHOFAMASs. The capability and advantage of OB-HOFAPC method are illustrated via numerical simulation and experimental verification on a cooperative flying-around task of three air-bearing spacecraft simulators.

Risk Modeling of Time-Varying Covariates Using an Ensemble of Survival Trees: Predicting Future Cancer Events

The challenge of survival prediction is ubiquitous in medicine, but only a handful of methods are available for survival prediction based on time-varying data. Here we propose a novel method for this problem, using a random forest of survival trees for left-truncated and right-censored data. We demonstrate the advantage of our method on prediction of breast cancer and prostate gland cancer risk among healthy individuals by analyzing routine laboratory measurements, vital signs and age. We analyze electronic medical records of 20,317 healthy individuals who underwent routine checkups and identified those who later developed cancer. In cross-validation, our method predicted future prostate and breast cancers six months before diagnosis with an area under the ROC curve of 0.62±0.05 and 0.6±0.03 respectively, outperforming standard random forest, random survival forest, cox-regression model, dynamic deep-hit and a single survival tree. Our work proposes a new framework for survival risk prediction in time-varying data and our results suggest that computational analysis of data on healthy individuals can improve the detection of those at risk of future cancer development.

Numerical simulation and projection of wind resources in the northern South China Sea

High-resolution assessment of future wind resources is crucial for formulating appropriate energy strategies. However, the response of offshore wind energy to changing climate remains inconclusive. This study uses bias-corrected CMIP6 and time-varying sea surface temperature data to drive the Weather Research and Forecasting (WRF) model and project future changes in wind energy resources in the northern South China Sea (SCS) under SSP2–4.5 and SSP5–8.5 scenarios for 2050 and 2100. The findings indicate that wind resources in the waters north of 18°N in the northern SCS generally show an increased change, with greater wind resources in 2100 than in 2050. The SSP5–8.5 scenario exhibits a more significant increase than the SSP2–4.5 scenario. The future wind energy changes in the western and eastern nearshore waters of Guangdong, where numerous offshore wind power plants are currently established, are largely consistent with the overall change in the northern SCS. Nevertheless, there may be a decreased change in future wind resources in the central and eastern regions of the SCS.

Nighttime Outdoor Artificial Light and Risk of Age-Related Macular Degeneration

Light pollution's impact on human health is increasingly recognized, but its link to exudative age-related macular degeneration (EAMD) remains unclear. To investigate the association between exposure to outdoor artificial light at night (OALAN) and the risk of incident EAMD. In this nationwide population-based case-control study, all individuals 50 years or older with newly diagnosed EAMD between January 1, 2010, and December 31, 2011, were identified with reference to the Korean National Health Insurance Service registration program database for rare and intractable diseases. Birth year- and sex-matched controls (with no EAMD diagnosis until 2020) were selected at a 1:30 ratio. Data were acquired from May 1 to December 31, 2021, and analyzed from June 1 to November 30, 2022. Mean levels of OALAN at participants' residential addresses during 2008 and 2009 were estimated using time-varying satellite data for a composite view of persistent nighttime illumination at an approximate scale of 1 km2. The hazard ratios (HRs) and 95% CIs of the association between residential OALAN and risk of incident EAMD were determined based on maximum likelihood estimation after adjusting for sociodemographic characteristics, comorbidities, and area-level risk factors (ie, nighttime traffic noise and particulate matter of aerodynamic diameter ≤10 μm in each participant's administrative district of residence). A total of 126 418 participants were included in the analysis (mean [SD] age, 66.0 [7.9] years; 78 244 men [61.9%]). Of these, 4078 were patients with newly diagnosed EAMD and 122 340 were EAMD-free matched controls. In fully adjusted models, an IQR (55.8 nW/cm2/sr) increase in OALAN level was associated with an HR of 1.67 (95% CI, 1.56-1.78) for incident EAMD. The exposure-response curve demonstrated a nonlinear, concave upward slope becoming more pronounced at higher levels of light exposure (ie, at approximately 110 nW/cm2/sr). In a subgroup analysis, an IQR increase in OALAN was associated with increased risk of incident EAMD in urban areas (HR, 1.46 [95% CI, 1.33-1.61]) but not in rural areas (HR, 1.01 [95% CI, 0.84-1.22]). In this nationwide population-based case-control study, higher levels of residential OALAN were associated with an increased risk of incident EAMD. Future studies with more detailed information on exposure, individual adaptive behaviors, and potential mediators are warranted.

STSR-INR: Spatiotemporal super-resolution for multivariate time-varying volumetric data via implicit neural representation

Implicit neural representation (INR) has surfaced as a promising direction for solving different scientific visualization tasks due to its continuous representation and flexible input and output settings. We present STSR-INR, an INR solution for generating simultaneous spatiotemporal super-resolution for multivariate time-varying volumetric data. Inheriting the benefits of the INR-based approach, STSR-INR supports unsupervised learning and permits data upscaling with arbitrary spatial and temporal scale factors. Unlike existing GAN- or INR-based super-resolution methods, STSR-INR focuses on tackling variables or ensembles and enabling joint training across datasets of various spatiotemporal resolutions. We achieve this capability via a variable embedding scheme that learns latent vectors for different variables. In conjunction with a modulated structure in the network design, we employ a variational auto-decoder to optimize the learnable latent vectors to enable latent-space interpolation. To combat the slow training of INR, we leverage a multi-head strategy to improve training and inference speed with significant speedup. We demonstrate the effectiveness of STSR-INR with multiple scalar field datasets and compare it with conventional tricubic+linear interpolation and state-of-the-art deep-learning-based solutions (STNet and CoordNet).

[formula omitted]JUpdate: An update language for time-varying JSON data

Time-varying JSON data are being used and exchanged in various today’s application frameworks like IoT platforms, Web services, cloud computing, online social networks, and mobile systems. However, in the state-of-the-art of JSON data management, there is neither a consensual nor a standard language for updating (i.e., inserting, modifying, and deleting) temporal JSON data, like the TSQL2 or SQL:2016 languages for temporal relational data. Moreover, existing JSON-based NoSQL DBMSs (e.g., MongoDB, Couchbase, CouchDB, OrientDB, and Riak) and both commercial relational DBMSs (e.g., IBM DB2 12, Oracle 19c, and MS SQL Server 2019) and open-source ones (e.g., PostgreSQL 15, and MySQL 8.0) supporting JSON documents do not provide any facility for maintaining temporal JSON data. Also in our previously proposed temporal JSON framework, called τJSchema, there was no feature for temporal JSON instance updates. For these reasons, we propose in this article a temporal update language, named τJUpdate (Temporal JUpdate), for JSON data in the τJSchema environment. We define it as a temporal extension of our previously introduced non-temporal JSON update language, named JUpdate (JSON Update). Both the syntax and the operational semantics of the data modification operations of JUpdate have been extended to support temporal aspects. τJUpdate allows to specify temporal JSON updates in an expressive and user-friendly manner, and to efficiently execute them in the τJSchema environment.

Dynamical programming for off-the-grid dynamic inverse problems

In this work we consider off-the-grid algorithms for the reconstruction of sparse measures from time-varying data. In particular, the reconstruction is a finite collection of Dirac measures whose locations and masses vary continuously in time. Recent work showed that this decomposition was possible by minimising a convex variational model which combined a quadratic data fidelity with dynamical Optimal Transport. We generalise this framework and propose new numerical methods which leverage efficient classical algorithms for computing shortest paths on directed acyclic graphs. Our theoretical analysis confirms that these methods converge to globally optimal reconstructions. Numerically, we show new examples for unbalanced Optimal Transport penalties, and for balanced examples we are 100 times faster in comparison to the previously known method.

Data-based bipartite formation control for multi-agent systems with communication constraints.

This article investigates data-driven distributed bipartite formation issues for discrete-time multi-agent systems with communication constraints. We propose a quantized data-driven distributed bipartite formation control approach based on the plant's quantized and saturated information. Moreover, compared with existing results, we consider both the fixed and switching topologies of multi-agent systems with the cooperative-competitive interactions. We establish a time-varying linear data model for each agent by utilizing the dynamic linearization method. Then, using the incomplete input and output data of each agent and its neighbors, we construct the proposed quantized data-driven distributed bipartite formation control scheme without employing any dynamics information of multi-agent systems. We strictly prove the convergence of the proposed algorithm, where the proposed approach can ensure that the bipartite formation tracking errors converge to the origin, even though the communication topology of multi-agent systems is time-varying switching. Finally, simulation and hardware tests demonstrate the effectiveness of the proposed scheme.

Height Variations in the Mississippi River Basin From Daily Time-Varying Satellite Gravity Data and Hydrological Model

Height Variations in the Mississippi River Basin From Daily Time-Varying Satellite Gravity Data and Hydrological Model

Numerical Calculation Method and Accuracy Analysis of Time-varying Data of Reluctance Coil Launcher

Numerical Calculation Method and Accuracy Analysis of Time-varying Data of Reluctance Coil Launcher

A time-varying copula approach for describing seasonality in multivariate ocean data

Characterizing multivariate ocean variables is quite critical for reliability design and risk assessment of marine structures. A robust, precise, and practical multivariate statistic model is necessary for comprehending ocean characteristics. As the time-varying characteristics exist in the ocean data, it is unreasonable to employ a simple constant statistical model to characterize all the multivariate data at one time. Therefore, in this paper, a time-varying copula approach is developed for modeling time-varying multivariate ocean data. Considering climate variations, a time-varying formula for return period and environment contour is also derived. The developed approach is demonstrated based on a site-specific ocean dataset collected from a buoy on the US coast. The climate effects associated with the multivariate ocean variables are characterized. The developed time-varying copula approach is also compared to the conventional copula and the conditional model in estimating the return period. The results showed that the time-varying model is helpful to explore the most critical environmental conditions for marine structures.

The Race to Exclude: Residential Growth Controls in California Cities, 1970–1992

Local regulations that restrict residential growth are a key driver of California’s affordable housing crisis. Scholars have argued these growth controls were implemented in the late 20th century by cities intending to exclude Black households. However, growth controls may also have plausibly been driven by a desire to exclude growing Hispanic, Asian, and foreign-born populations; by increased concern about the negative environmental consequences of population growth; or by homeowners’ or cities’ fiscal motivations. I jointly test these competing explanations using time-varying data on the adoption of a variety of residential growth controls covering California cities from 1970 to 1992. I find that, all else equal, cities with a lower share of Black residents—both in absolute terms, and relative to their metropolitan area—were more likely to pass residential growth controls. I also find some evidence that growth controls were more likely to be passed in areas experiencing greater Black population growth and in cities more supportive of White-Black segregation. Finally, I find strong evidence that, net of other factors, cities in areas more supportive of policies to protect the environment were more likely to pass residential growth controls.

Reconstructing higher-resolution four-dimensional time-varying volumetric data

We have witnessed substantial growth in super-resolution research within the computer vision community. Unlike previous works that mainly focus on the super-resolution synthesis of images, videos, or single volumes, our research is dedicated to the super-resolution synthesis of time-varying volumetric data, which are generated from scientific simulations and are crucial for domain scientists to understand and analyse complex scientific phenomena. Compared to previous works, our research presents a greater challenge: the time-varying volumetric data have higher dimensions, making it more difficult to synthesise super-resolution that maintains good spatio-temporal consistency while achieving high visual quality. To tackle this challenge, we introduce a new GAN-based network called SSR-DoubleUNetGAN, which includes novel network architecture and loss functions, allowing for accurate synthesis of spatial super-resolution for time-varying volumetric data with relatively fast training time. Our method can be applied in the context of in-situ visualisation to aid domain scientists in analysing more time-varying volumetric data more efficiently. In addition, it can be used in the compression-decompression pipeline to recover the super-resolution time-varying volumetric data from their low-resolution counterpart. To demonstrate its effectiveness, we applied various time-varying volumetric datasets from different scientific simulations to it. To demonstrate its advantages, we compared it qualitatively and quantitatively with five state-of-the-art super-resolution techniques, namely SSR-TVD, Tricubic, SRResNet, Cubic, and Linear. Furthermore, we conducted an ablation study to validate its important modules. The experimental results show that our method outperforms the compared state-of-the-art techniques.

Joint analysis and segmentation of time-varying data with outliers

Principal-Component Analysis (PCA) is a fundamental tool in data science and machine learning, used for compressing, analyzing, visualizing, and processing large datasets. At the same time, temporal segmentation is important for coherent component analysis of big data collections generated by time-varying distributions. However, both segmentation and PCA can be critically affected and misled by corrupted points that often exist in big data collections. To address these issues, we propose a novel and robust method for joint segmentation and principal-component analysis of time-varying data, based on L1-norm formulations. Our proposed method estimates robust L1-norm principal components (L1-PCs) over different temporal horizons and combines them to perform outlier detection, data segmentation, and subspace estimation. Numerical studies on real-world data, including videos and smartphone-sensed human body motion measurements, corroborate the merits of the proposed method in terms of segmentation, PCA, and outlier detection/removal.

Geophysical monitoring of the groundwater resources in the Southern Arabian Peninsula using satellite gravity data

In recent decades, geophysical and remote sensing monitoring techniques have advanced to the point where they can be utilized. It is possible to investigate the spatiotemporal mass fluctuations induced by groundwater changes over the Southern Arabian Peninsula (SAP) by combining time-variable gravity data with land surface model outputs and rainfall data. Here are the findings: The average annual precipitation rates for the whole study region were 91.11, 87.6, and 96.61 mm yr−1 during the entire period (2002–2021), period before 2013, and period after 2012, respectively. The southern and eastern parts (Zone I) of the investigated region show modest rainfall rates of 109.6, 105, and 117 mm yr−1 during the whole period, period before 2013, and period after 2012, respectively. The Rub El Khali region (Zone II) is receiving lower precipitation rates of 54.6, 53.3, and 56.5 mm yr−1 throughout the whole period, period before 2013, and period after 2012, respectively. Based on the three distinct gravity solutions, the average Terrestrial Water Storage (ΔTWS) values are computed through the entire period to be − 0.21 ± 0.011, − 0.15 ± 0.013, and − 0.32 ± 0.0107 cm yr−1 for the whole study region, Zone of the southern and eastern regions, and Zone of Rub El Khali, respectively. The whole study region, Zone of the southern and eastern parts, and Zone of Rub El Khali are showing highly negative ΔTWS in the period before 2013, in comparison to slightly negative to slightly positive ΔTWS trends in period after 2012. The average annual change in groundwater storage for the entire study area was calculated at − 0.21 ± 0.011, − 0.29 ± 0.024, and − 0.091 ± 0.038 cm yr−1 throughout the investigated period, period before 2013, and period after 2012, respectively. Zone of Rub El Khali is showing higher negative groundwater storage trend (ΔGWS) averaged at − 0.32 ± 0.104 cm yr−1 throughout the investigated period, whereas Zone of southern and eastern regions is showing lower negative groundwater storage trend of − 0.15 ± 0.013 cm yr−1. Most of the recharge rate occurs in Zone of the southern and eastern regions reaching up to + 0.77 ± 0.092 cm yr−1 by taking the average groundwater withdrawal rate of + 0.92 ± 0.092 cm yr−1 during the whole period. This integrated approach is a valuable and economical method for more effectively assessing the variations of groundwater resources across wide areas.

CoordNet: Data Generation and Visualization Generation for Time-Varying Volumes via a Coordinate-Based Neural Network.

Although deep learning has demonstrated its capability in solving diverse scientific visualization problems, it still lacks generalization power across different tasks. To address this challenge, we propose CoordNet, a single coordinate-based framework that tackles various tasks relevant to time-varying volumetric data visualization without modifying the network architecture. The core idea of our approach is to decompose diverse task inputs and outputs into a unified representation (i.e., coordinates and values) and learn a function from coordinates to their corresponding values. We achieve this goal using a residual block-based implicit neural representation architecture with periodic activation functions. We evaluate CoordNet on data generation (i.e., temporal super-resolution and spatial super-resolution) and visualization generation (i.e., view synthesis and ambient occlusion prediction) tasks using time-varying volumetric data sets of various characteristics. The experimental results indicate that CoordNet achieves better quantitative and qualitative results than the state-of-the-art approaches across all the evaluated tasks. Source code and pre-trained models are available at https://github.com/stevenhan1991/CoordNet.

Error correction algorithm of array time-varying amplitude and phase based on autoencoder

As array antennas are widely used in various mobile platforms, the time-varying amplitude and phase error has become an important factor affecting the application of array signal processing technology. A deep learning-based algorithm for the correction of time-varying amplitude and phase errors in arrays is proposed in terms of the idea of autoencoder. The algorithm makes full use of the data feature extraction and reconstruction capability of the autoencoder network, designs a deep learning network for the correction of time-varying amplitude phase error of the channel, gives a double-driven learning mechanism without time-varying amplitude phase error data (unperturbed data) and time-varying amplitude phase error data (perturbed data), completes the extraction of the array stream shape hidden features based on the principle of minimising the mean square error of the desired output and the ideal model. The simulated experiments show that the algorithm can effectively correct the time-varying amplitude and phase errors of each channel, and the mean square error of the corrected amplitude and phase errors are within 0.5% and 1.5% respectively when there are ±80% random time-varying amplitude errors and ±5° random time-varying phase errors. The effectiveness of the proposed algorithm is verified.