Spatiotemporal Robustness Research Articles

Mapping upland crop–rice cropping systems for targeted sustainable intensification in South China

Upland crop-rice cropping systems (UCR) facilitate sustainable agricultural intensification. Accurate UCR cultivation mapping is needed to ensure food security, sustainable water management, and rural revitalization. However, datasets describing cropping systems are limited in spatial coverage and crop types. Mapping UCR is more challenging than crop identification and most existing approaches rely heavily on accurate phenology calendars and representative training samples, which limits its applications over large regions. We describe a novel algorithm (RRSS) for automatic mapping of upland crop–rice cropping systems using Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multispectral Instrument (MSI) data. One indicator, the VV backscatter range, was proposed to discriminate UCR and another two indicators were designed by coupling greenness and pigment indices to further discriminate tobacco or oilseed UCR. The RRSS algorithm was applied to South China characterized by complex smallholder rice cropping systems and diverse topographic conditions. This study developed 10-m UCR maps of a major rice bowl in South China, the Xiang-Gan-Min (XGM) region. The performance of the RRSS algorithm was validated based on 5197 ground-truth reference sites, with an overall accuracy of 91.92%. There were 7348 km2 areas of UCR, roughly one-half of them located in plains. The UCR was represented mainly by oilseed-UCR and tobacco-UCR, which contributed respectively 69% and 15% of UCR area. UCR patterns accounted for only one-tenth of rice production, which can be tripled by intensification from single rice cropping. Application to complex and fragmented subtropical regions suggested the spatiotemporal robustness of the RRSS algorithm, which could be further applied to generate 10-m UCR datasets for application at national or global scales.

Robust Satisfaction of Metric Interval Temporal Logic Objectives in Adversarial Environments

This paper studies the synthesis of controllers for cyber-physical systems (CPSs) that are required to carry out complex time-sensitive tasks in the presence of an adversary. The time-sensitive task is specified as a formula in the metric interval temporal logic (MITL). CPSs that operate in adversarial environments have typically been abstracted as stochastic games (SGs); however, because traditional SG models do not incorporate a notion of time, they cannot be used in a setting where the objective is time-sensitive. To address this, we introduce durational stochastic games (DSGs). DSGs generalize SGs to incorporate a notion of time and model the adversary’s abilities to tamper with the control input (actuator attack) and manipulate the timing information that is perceived by the CPS (timing attack). We define notions of spatial, temporal, and spatio-temporal robustness to quantify the amounts by which system trajectories under the synthesized policy can be perturbed in space and time without affecting satisfaction of the MITL objective. In the case of an actuator attack, we design computational procedures to synthesize controllers that will satisfy the MITL task along with a guarantee of its robustness. In the presence of a timing attack, we relax the robustness constraint to develop a value iteration-based procedure to compute the CPS policy as a finite-state controller to maximize the probability of satisfying the MITL task. A numerical evaluation of our approach is presented on a signalized traffic network to illustrate our results.

State and Trait Anxiety Share Common Network Topological Mechanisms of Human Brain.

Anxiety is a future-oriented unpleasant and negative mental state induced by distant and potential threats. It could be subdivided into momentary state anxiety and stable trait anxiety, which play a complex and combined role in our mental and physical health. However, no studies have systematically investigated whether these two different dimensions of anxiety share a common or distinct topological mechanism of human brain network. In this study, we used macroscale human brain morphological similarity network and functional connectivity network as well as their spatial and temporal variations to explore the topological properties of state and trait anxiety. Our results showed that state and trait anxiety were both negatively correlated with the coefficient of variation of nodal efficiency in the left frontal eyes field of volume network; state and trait anxiety were both positively correlated with the median and mode of pagerank centrality distribution in the right insula for both static and dynamic functional networks. In summary, our study confirmed that state and trait anxiety shared common human brain network topological mechanisms in the insula and the frontal eyes field, which were involved in preliminary cognitive processing stage of anxiety. Our study also demonstrated that the common brain network topological mechanisms had high spatiotemporal robustness and would enhance our understanding of human brain temporal and spatial organization.

Spatial and temporal robustness for scheduling a target tracking mission using wireless sensor networks

Robust scheduling for target tracking with a wireless sensor network (WSN), focuses on the deployment of a WSN in a remote area to monitor a set of moving targets. Each sensor operates on a battery and is able to communicate with reachable sensors in the network. The targets are typically moving vehicles (planes, trains, cars,…) passing through the area. In order to monitor the targets, an activation schedule is sought such that the sensor network is continuously collecting data about the targets. Additionally, the transfer of the data collected to a base station deployed near the network also has to be planned. In this work, we consider that the trajectories of the targets are estimated. i.e., during the mission, at each time instant t, there is a given position where the target is expected. However, such estimations are inaccurate and deviations can occur. In this work, we formulate the problem of spatial robust scheduling. The aim is to produce an activation schedule for the sensors such that the targets are covered as long as they remain no farther from their estimated positions than a maximized value, called the spatial stability radius of the schedule. Afterwards, we formulate the spatio-temporal robustness problem. It is a bi-objective problem, with a spatial stability radius and a temporal stability radius for covering delays and advances. Two algorithms are proposed to solve these problems, and we show their efficiency through several numerical experiments.

Robustness and Precision of Bacterial Quorum Sensing in Fluid Flow

Quorum sensing allows bacterial cells to transfer information within populations and make collective measurements about aspects of their surroundings. In this process, quorum sensing molecules diffuse between cells and are therefore exposed to local external forces including advection by fluid flow, a ubiquitous feature of natural and industrial environments. However despite the recent design of experimental systems to test the macroscopic effects of fluid flow on intercellular signaling, the links between microscale genetic circuits and macroscale responses to steady or intermittent flow are not well understood. Here, we develop a theory that explains recent experimental observations by linking population-level spatial and temporal processes to cell-level genetic networks. Our theory predicts that positive feedback in signaling networks acts as a low pass filter that enables cells to respond to the average shear rate of a dynamically changing flow. This suggest that signaling systems with positive feedback act with spatiotemporal robustness in response to changes in fluid flow, whereas systems with negative feedback respond quickly and locally. Our results shed light on how various bacterial signaling systems are adapted to typical physiological environments.

Recent global and regional trends in burned area and their compensating environmental controls

The apparent decline in the global incidence of fire between 1996 and 2015, as measured by satellite-observations of burned area, has been related to socioeconomic and land use changes. However, recent decades have also seen changes in climate and vegetation that influence fire and fire-enabled vegetation models do not reproduce the apparent decline. Given that the satellite-derived burned area datasets are still relatively short (<20 years), this raises questions both about the robustness of the apparent decline and what causes it. We use two global satellite-derived burned area datasets and a data-driven fire model to (1) assess the spatio-temporal robustness of the burned area trends and (2) to relate the trends to underlying changes in temperature, precipitation, human population density and vegetation conditions. Although the satellite datasets and simulation all show a decline in global burned area over ~20 years, the trend is not significant and is strongly affected by the start and end year chosen for trend analysis and the year-to-year variability in burned area. The global and regional trends shown by the two satellite datasets are poorly correlated for the common overlapping period (2001–2015) and the fire model simulates changes in global and regional burned area that lie within the uncertainties of the satellite datasets. The model simulations show that recent increases in temperature would lead to increased burned area but this effect is compensated by increasing wetness or increases in population, both of which lead to declining burned area. Increases in vegetation cover and density associated with recent greening trends lead to increased burned area in fuel-limited regions. Our analyses show that global and regional burned area trends result from the interaction of compensating trends in controls of wildfire at regional scales.

A Generalized Model of Hourly Net Ecosystem Exchange (NEE) for Florida Everglades Freshwater Wetlands

A generalized model is presented to estimate the diurnal cycle of hourly net ecosystem exchange (NEE) based on a corresponding single reference-time observation from the Florida Everglades freshwater wetlands. The year-round diurnal cycles of NEE for two different (short vs. long hydroperiod) marsh sites were normalized by the corresponding day- and site-specific reference observations of NEE to obtain a common dimensionless cycle. An extended stochastic harmonic analysis (ESHA) was utilized to calibrate and validate the model with hourly eddy-covariance observations of NEE during 2008–13. The model involved five parameters, which exhibited spatiotemporal robustness by collapsing into narrow ranges among different days, years and sites. The daily estimates were averaged over all calibration days to calculate the site-specific ensemble parameter sets. The site-specific ensemble parameters were further averaged over four mid-day reference times (11 A.M. to 2 P.M.) across sites to obtain a generalized ensemble parameter set. Estimated hourly NEE using the site-specific and the generalized parameter sets indicated a good performance of the model (e.g., Nash-Sutcliffe Efficiency, NSE = 0.66–0.89). The model is represented in three standalone formats, including an Excel spreadsheet, to simulate the hourly NEE for the desired Julian days and years from the respective single reference observations.

On the reusage of primary radio resources through the exploitation of the spatiotemporal robustness of the MIMO transmissions

The spatiotemporal robustness of the MIMO transmissions is exploited aiming at inducing opportunistic secondary communications on the primary radio resources. The proposed model makes use of the smallest singular values and the Extreme Value Theory for characterizing the robustness of the MIMO systems, having always the premise of preventing significant degradations in the performance of the primary communications, while at the same time an attractive number of potential opportunities for secondary are intended to be offered. So, the idea behind the suggested scheme consists in reusing the information that is already available within the primary MIMO networks aiming at cotransmitting opportunistically in a clever manner, which at the end will allow us to make a much more efficient use of both the natural and technical resources.

Locally disrupted synchronization in Langevin molecular dynamics

Stochastic thermostats commonly used in molecular dynamics trajectories are known, under certain conditions, to exhibit a synchronization effect whereby trajectories initialized at different points in phase space synchronize to a single master trajectory if they are subjected to the same sequence of random forces. We investigate the spatiotemporal robustness of this effect analytically and with molecular dynamics simulations in one and three dimensions in the strong coupling limit. We first investigate the response of the system to a time- and spacewise local perturbation and show that desynchronization behaves diffusively at long times for infinite systems. We then explore the behavior of temporally persistent but spatially local perturbations and observe strikingly different behaviors as a function of dimensionality: in one dimension, the desynchronization propagates through the whole lattice and grows with time, while in three dimensions, the desynchronization remains localized in the neighborhood of the perturbation.

On the Reusage of Primary Radio Resources through the Exploitation of the Spatiotemporal Robustness of the MIMO Transmissions

The spatiotemporal robustness of the MIMO transmissions is exploited aiming at inducing opportunistic secondary communications on the primary radio resources. The proposed model makes use of the smallest singular values and the Extreme Value Theory for characterizing the robustness of the MIMO systems, having always the premise of preventing significant degradations in the performance of the primary communications, while at the same time an attractive number of potential opportunities for secondary are intended to be offered. So, the idea behind the suggested scheme consists in reusing the information that is already available within the primary MIMO networks aiming at cotransmitting opportunistically in a clever manner, which at the end will allow us to make a much more efficient use of both the natural and technical resources.

Self-Organizing Hierarchies in Sensor and Communication Networks

We consider a hierarchical multicellular sensing and communication network, embedded in an ageless aerospace vehicle that is expected to detect and react to multiple impacts and damage over a wide range of impact energies. In particular, we investigate self-organization of impact boundaries enclosing critically damaged areas, and impact networks connecting remote cells that have detected noncritical impacts. Each level of the hierarchy is shown to have distinct higher-order emergent properties, desirable in self-monitoring and self-repairing vehicles. In addition, cells and communication messages are shown to need memory (hysteresis) in order to retain desirable emergent behavior within and between various hierarchical levels. Spatiotemporal robustness of self-organizing hierarchies is quantitatively measured with graph-theoretic and information-theoretic techniques, such as the Shannon entropy. This allows us to clearly identify phase transitions separating chaotic dynamics from ordered and robust patterns.