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Spatiotemporal energy infrastructure datasets for the United States: A review
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Spatiotemporal characterization of the field-induced insulator-to-metal transition
Watching a metal filament grow Resistive switching is a process in which the electrical resistance of a sample changes abruptly in response to a voltage pulse, often by orders of magnitude. This process is at the heart of many neuromorphic computing approaches but visualizing it in both space and time is tricky. del Valle et al . monitored the resistive switching in three different vanadium oxide compounds by measuring time- and space-resolved optical reflectivity (see the Perspective by Hilgenkamp and Gao). A characteristic conducting filament was quickly nucleated on the inhomogeneities in the sample and then propagated due to Joule heating. —JS
A Spatiotemporal Sequence Forecasting Platform to Advance the Prediction of Changing Spatiotemporal Patterns of CO 2 Concentration by Incorporating Human Activity and Hydrological Extremes
Focal Areas (2): Predictive modeling using AI techniques and AI-derived model components. It describes the use of AI and other tools to design a prediction system comprising a hierarchy of models. Specifically, this white paper focuses on the development of a collaborative deep learning platform for evaluating and predicting spatiotemporal relationships between the hydrological and carbon cycles and includes capabilities for considering biogenic and anthropogenic inputs to these systems.
Spatiotemporal Characteristics and Propagation of Summer Extreme Precipitation Events over United States: A Complex Network Analysis
Complex Network (CN) is a graph-theory based depiction of relation shared by various elements of a complex-dynamical system such as the atmosphere. Here we applied the concept of CN to understand the directionality and topological structure of summer extreme precipitation events (SEPEs) over the conterminous United States (CONUS). The SEPEs are calculated based on the 95th percentile daily rainfall at 0.5ox0.5o spatial resolution for CONUS to investigate the multi-dimensional characteristics of precipitation extremes. The derived CN coefficients (e.g., betweenness centrality, clustering coefficient, orientation, and network divergence) reveal important structural and dynamical information about the topology of the SEPEs and improve understanding of the dominant meteorological patterns. The initiation and propagation of SEPEs from the source-zones to the sink-zones are identified. The SEPEs are influenced by topography, dominant wind patterns, and moisture sources in terms of their topological structure and spatial dynamics.
Evaluating a spatiotemporal shape-matching model for the generation of synthetic high spatiotemporal resolution time series of multiple satellite data
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Roadmap on spatiotemporal light fields
Spatiotemporal sculpturing of light pulse with ultimately sophisticated structures represents a major goal of the everlasting pursue of ultra-fast information transmission and processing as well as ultra-intense energy concentration and extraction. It also holds the key to unlock new extraordinary fundamental physical effects. Traditionally, spatiotemporal light pulses are always treated as spatiotemporally separable wave packet as solution of the Maxwell’s equations. In the past decade, however, more generalized forms of spatiotemporally nonseparable solution started to emerge with growing importance for their striking physical effects. This roadmap intends to highlight the recent advances in the creation and control of increasingly complex spatiotemporally sculptured pulses, from spatiotemporally separable to complex nonseparable states, with diverse geometric and topological structures, presenting a bird’s eye viewpoint on the zoology of spatiotemporal light fields and the outlook of future trends and open challenges.
Adaptation of primate vestibuloocular reflex to altered peripheral vestibular inputs. II Spatiotemporal properties of the adapted slow-phase eye velocity
1. The ability of the vestibuloocular reflex (VOR) to undergo adaptive modification after selective changes in the peripheral vestibular system was investigated in rhesus monkeys by recording three-dimensional eye movements before and after inactivation of selective semicircular canals. In the preceding paper we showed that the horizontal VOR gain evoked by passive yaw oscillations after lateral semicircular canal inactivation recovers gradually over time in a frequency-specific manner. Here we present the spatial tuning of the adapted slow-phase eye velocity and describe its spatiotemporal properties as a function of time after canal inactivation. 2. The spatial organization of the VOR was investigated during oscillations at different head positions in the pitch, roll, and yaw planes, as well as in the right anterior/left posterior and left anterior/right posterior canal planes. Acutely after bilateral inactivation of the lateral semicircular canals, a small horizontal response could still be elicited that peaked during rotations in pitched head positions that would maximally stimulate vertical semicircular canals. In addition, the phase of horizontal slow-phase velocity abruptly reversed through 180 degrees at positions close to upright, similarly to torsional slow-phase velocity. These spatial response properties suggest that the small, residual horizontal response components that are present acutely after plugging of both lateral canals originate from vertical semicircular canal signals. 3. As the horizontal response amplitude increased over time, consistent changes were also observed in the spatiotemporal tuning of horizontal slow-phase velocity. 1) The spatiotemporal response properties of horizontal slow-phase velocity acquired noncosine tuning characteristics, primarily in the pitch plane, in the right anterior/left posterior and left anterior/right posterior canal planes. Accordingly, horizontal response amplitude was nonzero during rotation in any head position in these planes and response phase varied significantly as a function of head orientation. 2) The peak horizontal response amplitude shifted spatially over time, such that 5-10 mo after plugging it was maximal during rotations at head positions close to upright. 4. In parallel to these unique spatiotemporal response properties characterizing the adapted horizontal VOR, torsional slow-phase velocity also exhibited small spatiotemporal changes after lateral canal inactivation that tended to precede in time the changes associated with the horizontal response components. In contrast, vertical slow-phase velocity in the plugged animals was unaltered and continued to be characterized by cosine-tuned spatial properties in three dimensions. 5. Recovery of the horizontal response gain during yaw oscillations in upright position, as well as the unique, noncosine spatiotemporal characteristics of the adapted horizontal VOR, were also observed in an animal with all but one vertical semicircular canals inactivated. There was, however, no sign of VOR gain recovery up to 2 mo after all semicircular canals were inactivated. These results suggest that the observed recovery of horizontal VOR is at least partly due to signals originating from the remaining intact vertical canal(s). Even in the presence of a single intact vertical canal, the improvement in horizontal gaze stability is at least partly restored through spatiotemporal changes in the processing of vestibuloocular signals that improve the gain and spatial tuning of horizontal VOR at the expense of temporal response properties.
Spatiotemporal pattern detection, generation, and computation with circuits
Abstract Implementations of neurons, delays, and synapse circuits are presented with simulations. These neural elements are used to create two small spiking neural networks, the Rate-Window and Order-Biased clusters, which are capable of detecting simple two-spike spatiotemporal patterns. A simple pattern detecting network (SPDN) is created by combining the Rate-Window and Order-Biased clusters, where clusters are small spiking neural networks, and its simple pattern detection ability is demonstrated in simulation. The SPDN is used to implement a complex pattern detecting network (CPDN) and its complex pattern detection ability is demonstrated in simulation. Methods for generating arbitrary spatiotemporal patterns are presented. The CPDN and spatiotemporal pattern generation methods are then used to implement a novel spatiotemporal computing paradigm based on detecting and responding to spatiotemporal symbols. A simulation of a spatiotemporal half adder is presented to demonstrate the computing paradigm.
A bi-level spatiotemporal clustering approach and its application to drought extraction
We present a novel flexible bi-level spatiotemporal clustering algorithm to extract events based on their intensity and spatiotemporal structures. Our algorithm consists of using (i) a novel space-time k-means clustering to obtain spatiotemporally coherent intensity clusters, and (ii) a density-based spatial clustering of applications with noise (DBSCAN) to spatiotemporally section the intensity clusters into individual events. We discuss the development of the algorithm, the selection, tuning and meaning of the parameters within each step, as well as its validation. Finally, we apply the algorithm to a spatiotemporal drought index, standardized vapor pressure deficit drought index (SVDI), over the continental United States (US) from 1980–2021 and show that it captures historical drought events over the continental United States and their spatiotemporal extents.
Bioenergy Cropping Reduces the Spatiotemporal Scaling of Soil Bacterial Biodiversity
Widespread bioenergy cropping can transform landscapes, strongly affecting biodiversity. However, the impact of bioenergy cropping on the spatiotemporal scaling of soil biodiversity remains virtually unknown, despite its profound implications for the functioning of the ecological community. Here, we investigated how bioenergy cropping influenced the spatiotemporal scaling of soil bacterial biodiversity in marginal soils (sandy loam and clay loam soils) in Oklahoma, USA. We detected strong, significant species-time-area relationships (STARs) and phylogenetic-time-area relationships (PTARs) in bacterial communities and their lineages, suggesting that STARs and PTARs exist in microbial ecology within the studied system. Also, spatiotemporal scaling rates (the slopes of STAR and PTAR models) varied substantially among bacterial lineages and were positively correlated with their 16S rRNA gene copy numbers, a genomic trait indicative of microbial growth potentials. Strikingly, bioenergy cropping significantly reduced spatiotemporal scaling rates by 6.8%-14.1%, with a more pronounced reduction observed in sandy loam soils, where those rates were significantly lower than in clay loam soils. The heterogeneity of soil phosphorus and carbon resulted in variations in bacterial spatiotemporal scaling rates. Collectively, our findings suggest that bioenergy cropping may alleviate rapid shifts in soil biodiversity across space and time, thereby stabilizing soil biodiversity and supporting its role as part of sustainable land management and climate mitigation strategies.
A Spatiotemporal Indexing Approach for Efficient Processing of Big Array-Based Climate Data with MapReduce
Climate observations and model simulations are producing vast amounts of array-based spatiotemporal data. Efficient processing of these data is essential for assessing global challenges such as climate change, natural disasters, and diseases. This is challenging not only because of the large data volume, but also because of the intrinsic high-dimensional nature of geoscience data. To tackle this challenge, we propose a spatiotemporal indexing approach to efficiently manage and process big climate data with MapReduce in a highly scalable environment. Using this approach, big climate data are directly stored in a Hadoop Distributed File System in its original, native file format. A spatiotemporal index is built to bridge the logical array-based data model and the physical data layout, which enables fast data retrieval when performing spatiotemporal queries. Based on the index, a data-partitioning algorithm is applied to enable MapReduce to achieve high data locality, as well as balancing the workload. The proposed indexing approach is evaluated using the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) climate reanalysis dataset. The experimental results show that the index can significantly accelerate querying and processing (10 speedup compared to the baseline test using the same computing cluster), while keeping the index-to-data ratio small (0.0328). The applicability of the indexing approach is demonstrated by a climate anomaly detection deployed on a NASA Hadoop cluster. This approach is also able to support efficient processing of general array-based spatiotemporal data in various geoscience domains without special configuration on a Hadoop cluster.
Limiting Data Friction by Reducing Data Download Using Spatiotemporally Aligned Data Organization Through STARE
Current data processing practice limits the volume and variety of relevant geoscience data that can practically be applied to important problems. File archives in centralized data centers are the principal means by which Earth Science data are accessed. This approach, however, requires laborious search, retrieval, and eventual customization/adaptation for the data to be used. Such fractionation makes it even more difficult to share outcomes, i.e. research artifacts and data products, hampering reusability and repeatability, since end users generally have their own research agenda and preferences as well as scarce resources. Thus, while finding and downloading data files from central data centers are already costly for end users working in their own field, using data products from other disciplines rapidly becomes prohibitive. This curtails scientific productivity, limits avenues of study, and endangers quality and reproducibility. The Spatio-Temporal Adaptive Resolution Encoding (STARE) is a unifying scheme that facilitates the indexing, access, and fusion of diverse Earth Science data. STARE implements an innovative encoding of geo-spatiotemporal information, originally developed for aligning datasets with diverse spatiotemporal characteristics in an array database. The spatial component of STARE recursively quadfurcates a root polyhedron, producing a hierarchical scheme for addressing geographic locations and regions. The temporal component of STARE uses conventional date-time units as an indexing hierarchy. The additional encoding of spatial and temporal resolution information in STARE enables comparisons and conditional selections across diverse datasets. Moreover, spatiotemporal set-operations, e.g. union and intersection, are mapped to efficient integer operations with STARE. Applied to existing data models (point, grid, spacecraft swath) and corresponding granules, STARE indexes provide a streamlined description usable as geo-spatiotemporal metadata. When coupled with large scale, distributed hardware and software, STARE-based data access reduces pre-analysis data preparation costs by offering a convenient means to align different datasets spatiotemporally without specialized effort in parallel computing or distributed data management.
Analyzing inference workloads for spatiotemporal modeling
Ensuring power grid resiliency, forecasting climate conditions, and optimization of transportation infrastructure are some of the many application areas where data is collected in both space and time. Spatiotemporal modeling is about modeling those patterns for forecasting future trends and carrying out critical decision-making by leveraging machine learning/deep learning. Once trained offline, field deployment of trained models for near real-time inference could be challenging because performance can vary significantly depending on the environment, available compute resources and tolerance to ambiguity in results. Users deploying spatiotemporal models for solving complex problems can benefit from analytical studies considering a plethora of system adaptations to understand the associated performance-quality trade-offs. To facilitate the co-design of next-generation hardware architectures for field deployment of trained models, it is critical to characterize the workloads of these deep learning (DL) applications during inference and assess their computational patterns at different levels of the execution stack. In this paper, we develop several variants of deep learning applications that use spatiotemporal data from dynamical systems. We study the associated computational patterns for inference workloads at different levels, considering relevant models (Long short-term Memory, Convolutional Neural Network and Spatio-Temporal Graph Convolution Network), DL frameworks (Tensorflow and PyTorch), precision (FP16, FP32, AMP, INT16 and INT8), inference runtime (ONNX and AI Template), post-training quantization (TensorRT) and platforms (Nvidia DGX A100 and Sambanova SN10 RDU). Overall, our findings indicate that although there is potential in mixed-precision models and post-training quantization for spatiotemporal modeling, extracting efficiency from contemporary GPU systems might be challenging. Instead, co-designing custom accelerators by leveraging optimized High Level Synthesis frameworks (such as SODA High-Level Synthesizer for customized FPGA/ASIC targets) can make workload-specific adjustments to enhance the efficiency.
Self-consistent dispersal puts tight constraints on the spatiotemporal organization of species-rich metacommunities
Dispersal can be critical to the maintenance of ecosystems as it allows local communities to be recolonized after extinction. However, it remains unclear whether the extinction-mitigating effect of dispersal persists when the number of competing species is large. Based on a spatially explicit mathematical description of metacommunities, we show that when many species coexist, each species operates near its extinction threshold, barely surviving due to dispersal. This has general consequences for spatiotemporal abundance patterns. For short-range dispersal, species organize into fractal spatiotemporal extinction patterns characteristic of a directed percolation phase transition. As species approach their extinction threshold, biodiversity is very sensitive to perturbation, suggesting that dispersal within a metacommunity puts tight constraints on the robustness and evolution of species-rich metacommunities. Biodiversity is often attributed to a dynamic equilibrium between the immigration and extinction of species. This equilibrium forms a common basis for studying ecosystem assembly from a static reservoir of migrants—the mainland. Yet, natural ecosystems often consist of many coupled communities (i.e., metacommunities), and migration occurs between these communities. The pool of migrants then depends on what is sustained in the ecosystem, which, in turn, depends on the dynamic migrant pool. This chicken-and-egg problem of survival and dispersal is poorly understood in communities of many competing species, except for the neutral case—the “unified neutral theory of biodiversity.” Employing spatiotemporal simulations and mean-field analyses, we show that self-consistent dispersal puts rather tight constraints on the dynamic migration–extinction equilibrium. When the number of species is large, species are pushed to the edge of their global extinction, even when competition is weak. As a consequence, the overall diversity is highly sensitive to perturbations in demographic parameters, including growth and dispersal rates. When dispersal is short range, the resulting spatiotemporal abundance patterns follow broad scale-free distributions that correspond to a directed percolation phase transition. The qualitative agreement of our results for short-range and long-range dispersal suggests that this self-organization process is a general property of species-rich metacommunities. Our study shows that self-sustaining metacommunities are highly sensitive to environmental change and provides insights into how biodiversity can be rescued and maintained.
Single-shot complete spatiotemporal measurement of terawatt laser pulses
In this work, we demonstrate, for the first time, a single-shot, complete spatiotemporal measurement of pulses from a terawatt-scale, multi-stage-amplified, low repetition-rate laser source. The ultrashort pulse electric field, E(x,y,z,t), is spatiotemporally complex due to distortions that accrue from multiple chirped-pulse amplifiers, which requires a complete characterization. Meanwhile, the instability of the laser source introduces field profiles that vary significantly from pulse to pulse, which, together with the low repetition-rate (15 shots/hour), requires the use of a single-shot measurement technique. To accomplish the measurements, we used a wavelength-multiplexed, digital-holographic technique called Spatially and Temporally Resolved Intensity and Phase Evaluation Device: Full Information from a Single Hologram, specially tailored to measure picosecond pulses at a wavelength of about 1 μm. Specifically, individual pulses from the compact multipulse terawatt laser were measured, with up to 0.3 J per shot of energy and ~2 ps pulse durations, at 1052 nm. With these measurements, we characterized several major spatiotemporal distortions that affect the peak intensity at the laser focus, as well as the pulse-shape instability on a shot-to-shot basis. Our technique allows detailed diagnosis of laser pulses (especially high-order spatiotemporal distortions) and provides straightforward four-dimensional animations of pulse propagation to a focus.