Clustering Of Events Research Articles

Does patient-ventilator asynchrony really matter?

Past observational studies have reported the association between patient-ventilator asynchronies and poor clinical outcomes, namely longer duration of mechanical ventilation and higher mortality. But causality has remained undetermined. During the era of lung and diaphragm protective ventilation, should we revolutionize our clinical practice to detect and treat dyssynchrony? Clinicians' ability to recognize asynchronies is typically low. Automatized softwares based on artificial intelligence have been trained to largely outperform human eyesight and are close to be implemented at the bedside. There is growing evidence that in susceptible patients, dyssynchrony may lead to ventilation-induced lung injury (or patient self-inflicted lung injury) and that clusters of such dyssynchronous events have the highest association with poor outcomes. Dyssynchrony may also be associated with harm indirectly when it reflects over-assistance or over-sedation. However, the occurrence of reverse triggering by means of low inspiratory efforts during passive ventilation may prevent diaphragm dysfunction and atrophy and be beneficial. Most recent evidence on the topic suggests that synchrony between the patient and the mechanical ventilator is a critical element for protecting lung and diaphragm during the time of invasive mechanical ventilation or may reflect inadequate settings or sedation. Therefore, it is a complex situation, and clinical trials are still needed to test the effectiveness of keeping patient-ventilator interaction synchronous on clinical outcomes.

An adaptive 6-dimensional floating-search multi-station seismic-event detector (A6-DFMSD) and its application to low-frequency earthquakes in the East Eifel Volcanic Field, Germany

We introduce a seismic event detector that applies signal analysis in the time and frequency domains. Signals are searched for with matching coincidences at neighbouring recording stations in space and time. No a priori waveform information is needed for the Adaptive 6-Dimensional Floating-search Multi-station Seismic-event Detector (A6-DFMSD). It combines a short / long time average algorithm (STA/LTA), frequency range selection, energy envelope matching, and backprojection techniques to find a robust detection model. As a challenging test example, the new detector is tuned and applied to a dataset with five months of microearthquake (ML < 2) recordings in the East Eifel Volcanic Field (EEVF), Germany. There, both magmatic and tectonic earthquakes occur in a depth range between 3 km and 43 km. A6-DFMSD detected 4.3 times as many events as were already known and it discovered a previously unknown event cluster. After manual localization and classification of the events, we show that A6-DFMSD finds events of different origins: tectonic, magmatic, atmospheric, and anthropogenic. In particular, low-frequency (LF) earthquakes of magmatic origin with a complicated waveform coda are very well identified. We suggest that seismological networks monitoring local seismicity in similar target zones would benefit from the use of A6-DFMSD to allow the detection of a wide range of different seismic signals.

Reconstructing a georeferenced inventory of flooding hazards in Raoping, Guangdong province, China, from 1492 to 1985

ABSTRACT In ancient times, there is a scarcity of instrumental water flow data, which challenges the hydrological science community to understand the evolution of flood risk under the changing climate over the centennial scale. Based on the historical records of flood events, the first spatio-temporal database of flood risk occurrence was established in Raoping county, Guangdong, China from 1492 to 1985 (a 494-year period), with intensive human interpretation and local investigation. Specifically, the spatially explicit flood events of the river network were provided for each flood episode. A detailed analysis of the database shows a high frequency of flood risk in summer and early autumn. Specifically, a significant (p &amp;lt; 0.05) increase has been found in flood risks since 1960. However, flooding decreased significantly in recent decades due to meteorological and hydrological factors, as well as the population density and migration during the 500-year period. A spatial clustering of flood events in the northern and southern parts is also confirmed, which shows an impact of population dynamics on a centennial scale. Such methods can be a reference for establishing China's flooding database for ancient periods, promoting a better understanding of natural hazards and associated human behaviors under the context of long-term climate change.

A Geometric View of Seismic Wavefields: Implications for Imaging Dense Clusters of Events

Summary Imaging dense clusters of seismicity is crucial to many problems in seismology: to delineate complex systems of faults, provide constraints on the causes of volcanic and cryogenic swarms, and to shed light on possible means to prevent damaging induced seismicity in mining, geothermal and oil and gas extraction activities. Current imaging methods rely upon high-resolution relative location techniques, commonly requiring arrival-time picks for seismic phases. This paper examines an alternative approach, based upon concepts drawn from differential geometry, that images directly from waveform data. It relies upon the common assumption of spatial continuity of seismic wavefield observations, which implies that a differentiable map exists between the source region to be imaged and waveform observations considered as elements of a vector space. The map creates an image of event clusters on a Riemannian manifold embedded in that vector space. The image can be visualized by projecting the observations into a tangent space of the manifold and is a distorted rendering of cluster geometry. However, the distortion can be predicted and removed if a model for wavefield propagation is available. This visualization approach is applicable to clusters of uniform events with highly similar waveforms, such as are commonly acquired with correlation detectors or other pattern matching techniques. To assess its performance, it is applied to the closely-related reciprocal problem of imaging the (known) geometry of an array from observations by the array of several regional events. Differences between the original problem and its reciprocal analog are noted and controlled for in the analysis. Chief among the differences is the necessity for aligning the waveforms in the original problem, which, to maintain consistency with the original problem, is solved in the reciprocal problem by a generalization of the VanDecar-Crosson algorithm. The VanDecar-Crosson algorithm exhibits a bias, shown through an analysis of the situation when the observed wavefields are adequately modeled as plane waves. In that circumstance, the bias can be predicted and removed. In a test using a portion of a large-N array, this imaging approach is shown to successfully reconstruct the array geometry. The method is applicable directly to infinitesimal array apertures, but is extended to a larger aperture by partitioning the image into local, effectively infinitesimal overlapping subsets. These are inverted, then assembled into a global picture of the array geometry using constraints provided by the overlapped regions. Although demonstrated in a reciprocal array context, the method appears viable for imaging clusters of events with highly similar source mechanisms and time histories.

Deep CO2 emissions facilitated by localized shear deformation: A case study of the Karakoram fault system, western Tibet

Strike-slip faults play a significant role in creating deeply penetrating fractures with high permeability, thus promoting rapid migration of CO2-rich fluids to the surface. However, there are rare observations regarding how strike-slip movement could affect deep CO2 emissions. Here, we focus on the Karakoram fault system (KKFS), western Tibet, to estimate diffuse soil CO2 fluxes and to unravel potential controlling factors for CO2 emissions. Average CO2 fluxes of geothermal fields range in 22–2475 g m−2 d−1, significantly higher than the across-fault profiles (6–116 g m−2 d−1). A mass balance model based on δ13C-CO2 and CO2 concentration of soil gases reveals that deep carbon constitutes 49.1–91.5 % (average = 73.9 %) and 0.2–40.5 % (average = 25.5 %) of soil-gas carbon released from geothermal fields and across-fault profiles, respectively. Deep carbon could be produced by thermal decomposition of crustal rocks considering CO2-rich fluids with radiogenic helium isotopes. Strikingly, higher CO2 fluxes preferentially occur in geothermal fields along a bending segment of the KKFS, where localized shear deformation is prominent as documented by high slip rates over geological timescales, dense splay faults, clustering of earthquake events, and elevated strain rates. We suggest that high stress acting on the KKFS bend could enhance the deformation and fracturing of fault zone rocks, leading to production of metamorphic CO2 and efficient release of CO2-rich fluids through the highly permeable fault system. Our results could shed new light on CO2 origins and fluxes of strike-slip faults that are characterized by spatially heterogeneous strain partitioning and thus localized enhanced shear deformation.

Hypergraph Convolutional Networks with Multi-Ordering Relations for Cross-Document Event Coreference Resolution

Recognizing the coreference relationship between different event mentions in the text (i.e., event coreference resolution) is an important task in natural language processing. It helps to understand the association between various events in the text, and plays an important role in information extraction, question answering systems, and reading comprehension. Existing research has made progress in improving the performance of event coreference resolution, but there are also some shortcomings. For example, most of the existing methods analyze the event data in the document in a serial processing mode, without considering the complex relationship between events, and it is difficult to mine the deep semantics of events. To solve these problems, this paper proposes a cross-document event co-reference resolution method (HGCN-ECR) based on hypergraph convolutional neural networks. Firstly, the BiLSTM-CRF model was used to label the semantic role of the events extracted from a number of scientific and technical kinds of literature. According to the labeling results, the trigger words and non-trigger words of the event were determined, and the multi-document event hypergraph was constructed around the event trigger words. Then hypergraph convolutional neural networks are used to learn higher-order semantic information in multi-document event hypergraphs, and multi-head attention mechanisms are introduced to understand the hidden features of different event relationship types by treating each event relationship as a set of separate attention mechanisms. Finally, the feed-forward neural network and the average link clustering method are used to calculate the coreference score of events and complete the coreference event clustering, and the cross-document event coreference resolution is realized. The experimental results show that the cross-document event co-reference resolution method is superior to the baseline model.

Design and characterization of a hybrid PET detector with DOI capability.

Monolithic or semi-monolithic detectors are attractive for positron emission tomography (PET) scanners with depth-of-interaction (DOI) capability. However, they often require complicated calibrations to determine the interaction positions of gamma photons. We introduce a novel hybrid detector design that combines pixelated and semi-monolithic elements to achieve DOI capability while simplifying the calibrations for positioning. A prototype detector with eight hybrid lutetium-yttrium oxyorthosilicate (LYSO)layers having dimensions of 25.8×12.9×15mm3 was constructed. The energy-weighted and energy-squared weighted averages were used for estimating the x- (pixelated direction) and y-positions (non-pixelated direction). Pseudo-pixels were defined as discrete areas on the flood image based on the crystal look-up table (LUT). The intrinsic spatial resolutions in the pixelated and non-pixelated directions were measured. The ratio of the maximum to the sum of the multipixel photon counter (MPPC) signals was used to estimate the DOI positions. The coincidence timing resolution (CTR) was measured using the average and energy-weighted average of the earliest n time stamps. Two energy windows of 250-700 and 400-600keV were applied for the measurements. The pattern of the flood images showed discrete event clusters, demonstrating that simple calibrations for determining the x- and y-positions of events could be achieved. Under 400-600keV energy window, the average intrinsic spatial resolutions were 1.15 and 1.34mm for the pixelated and non-pixelated directions; the average DOI resolution of the second row of pseudo-pixels was 5.1mm in full width at half maximum (FWHM); when using the energy-weighted average of the earliest four-time stamps, the best CTR of 350ps was achieved. Applying a broader energy window of 250-700keV only slightly degrades the DOI resolution while maintaining the intrinsic resolution; the best CTR degrades to 410ps. The proposed hybrid detector concept was verified, and a prototype detector showed high performance for 3D positioning and timing resolution. The novel detector concept shows promise for preclinical and clinical PET scanners with DOI capability.

A nonstationary stochastic simulator for clustered regional hydroclimatic extremes to characterize compound flood risk

Traditional approaches to flood risk management assume flood events follow an independent, identically distributed (i.i.d.) random process from which static risk measures are computed. Modern risk accounting strategies also consider nonstationarity or long-term trends in the mean and moments of the associated flood probability distributions. However, few approaches consider how extreme hydroclimatic events cluster in both space and time, compounding damage risks. Here we introduce a compound flood risk simulator that models and conditionally forecasts future variability in regional flooding events that cluster in time, given trends and oscillations in a variable climate signal. A modular, novel integration of wavelet signal processing, nonstationary time series forecasting, k-nearest neighbor (KNN) bootstrapping, multivariate copulas, and modified Neyman-Scott (NS) event clustering process provides users the ability to model interannual and sub-annual clustering of flood risk. Our semi-parametric flood generator specifically targets the clustered temporal dynamics of jointly modeled flood intensity, duration, and frequency over a finite future period of a decade or more, thereby providing a foundation for adaptation approaches that integrate temporally clustered flood risk into planning, response and recovery.

Understanding overfitting in random forest for probability estimation: a visualization and simulation study

BackgroundRandom forests have become popular for clinical risk prediction modeling. In a case study on predicting ovarian malignancy, we observed training AUCs close to 1. Although this suggests overfitting, performance was competitive on test data. We aimed to understand the behavior of random forests for probability estimation by (1) visualizing data space in three real-world case studies and (2) a simulation study.MethodsFor the case studies, multinomial risk estimates were visualized using heatmaps in a 2-dimensional subspace. The simulation study included 48 logistic data-generating mechanisms (DGM), varying the predictor distribution, the number of predictors, the correlation between predictors, the true AUC, and the strength of true predictors. For each DGM, 1000 training datasets of size 200 or 4000 with binary outcomes were simulated, and random forest models were trained with minimum node size 2 or 20 using the ranger R package, resulting in 192 scenarios in total. Model performance was evaluated on large test datasets (N = 100,000).ResultsThe visualizations suggested that the model learned “spikes of probability” around events in the training set. A cluster of events created a bigger peak or plateau (signal), isolated events local peaks (noise). In the simulation study, median training AUCs were between 0.97 and 1 unless there were 4 binary predictors or 16 binary predictors with a minimum node size of 20. The median discrimination loss, i.e., the difference between the median test AUC and the true AUC, was 0.025 (range 0.00 to 0.13). Median training AUCs had Spearman correlations of around 0.70 with discrimination loss. Median test AUCs were higher with higher events per variable, higher minimum node size, and binary predictors. Median training calibration slopes were always above 1 and were not correlated with median test slopes across scenarios (Spearman correlation − 0.11). Median test slopes were higher with higher true AUC, higher minimum node size, and higher sample size.ConclusionsRandom forests learn local probability peaks that often yield near perfect training AUCs without strongly affecting AUCs on test data. When the aim is probability estimation, the simulation results go against the common recommendation to use fully grown trees in random forest models.

Unsupervised clustering of mining-induced microseismicity provides insights into source mechanisms

Microseismic source mechanisms in underground mines can provide information about the rock mass response to mining. Conventional approaches to such studies rely upon moment tensor solutions that are susceptible to modeling assumptions and need reliable information about source locations and high-resolution velocity models. We propose the application of unsupervised clustering to group microseismic events into different classes directly from the waveform data such that the events in a specific class have similar source mechanisms. Our method has three main steps, first using spectral decomposition to separate the source terms from the path-receiver contributions in the observed amplitude spectra of events occurring in spatially dense clusters. Second, reducing the number of features from the source spectra using independent component analysis (ICA). Third, applying a Gaussian mixture model (GMM) to the reduced feature matrix to obtain event clusters. To test our method, we generate synthetic waveform data using the receiver network and the recorded microseismic event locations in an underground potash mine in Saskatchewan. Results show the ability of our method to separate events into different classes corresponding to differences in source mechanisms. Application to field data recorded in the mine during February 2021 successfully discriminates between blasts and microseismic events. The data recorded between 1 March and 30 June 2021 that contain microseismic events only are divided into two dominant classes. Using known moment tensors (MT) of some of these events for labeling, we interpret one of the two classes as having dominant double-couple mechanisms as compared to the other which most likely corresponds to the linear dipole-tensile mechanisms. Our method, combined with some expert knowledge such as MT of some larger magnitude events, can offer an assessment of source types of large microseismic populations as often encountered in induced seismicity.

Signatures of criticality in turning avalanches of schooling fish

Moving animal groups transmit information through propagating waves or behavioral cascades, exhibiting characteristics akin to systems near a critical point from statistical physics. Using data from freely swimming schooling fish in an experimental tank, we investigate spontaneous behavioral cascades involving turning avalanches, where large directional shifts propagate across the group. We analyze several avalanche metrics and provide a detailed picture of the dynamics associated with turning avalanches, employing tools from avalanche behavior in condensed-matter physics and seismology. Our results identify power-law distributions and robust scale-free behavior through data collapses and scaling relationships, confirming a necessary condition for criticality in fish schools. We explore the biological function of turning avalanches and link them to collective decision-making processes in selecting a new movement direction for the school. We report relevant boundary effects arising from interactions with the tank walls and influential roles of boundary individuals. Finally, spatial and temporal correlations in avalanches are explored using the concept of aftershocks from seismology, revealing clustering of avalanche events below a designated timescale and an Omori law with a faster decay rate than observed in earthquakes. Published by the American Physical Society 2024

Machine Learning-Driven Quantification of CO2 Plume Dynamics at Illinois Basin Decatur Project Sites Using Microseismic Data

This study utilizes machine learning to quantify CO2 plume extents by analyzing microseismic data from the Illinois Basin Decatur Project (IBDP). Leveraging a unique dataset of well logs, microseismic records, and CO2 injection metrics, this work aims to predict the temporal evolution of subsurface CO2 saturation plumes. The findings illustrate that machine learning can predict plume dynamics, revealing vertical clustering of microseismic events over distinct time periods within certain proximities to the injection well, consistent with an invasion percolation model. The buoyant CO2 plume partially trapped within sandstone intervals periodically breaches localized barriers or baffles, which act as leaky seals and impede vertical migration until buoyancy overcomes gravity and capillary forces, leading to breakthroughs along vertical zones of weakness. Between different unsupervised clustering techniques, K-Means and DBSCAN were applied and analyzed in detail, where K-means outperformed DBSCAN in this specific study by indicating the combination of the highest Silhouette Score and the lowest Davies–Bouldin Index. The predictive capability of machine learning models in quantifying CO2 saturation plume extension is significant for real-time monitoring and management of CO2 sequestration sites. The models exhibit high accuracy, validated against physical models and injection data from the IBDP, reinforcing the viability of CO2 geological sequestration as a climate change mitigation strategy and enhancing advanced tools for safe management of these operations.

Ikaros Deletions among Bulgarian Patients with Acute Lymphoblastic Leukemia/Lymphoma.

The Ikaros zinc finger factor 1 is a transcription factor with a well-known role in B- and T-cell development. The deletions of IKZF1 have an established significance in acute lymphoblastic leukemia, while reports on its prevalence and prognostic significance among ALL subtypes and regions vary. Breakpoint-specific qPCR is a practical method for testing of the most frequent types of IKZF1 deletions, considering there is clustering of the deletion events. The most commonly reported deletions are Δ4-7, Δ4-8, Δ2-7, and Δ2-8, with deletion Δ4-7 being the most common one. We retrospectively administered a breakpoint-specific qPCR design for screening for the most frequent types of IKZF1 deletions to 78 ALL patients that were diagnosed and treated between 2010 and 2022. We observed the products through gel electrophoresis, and we conducted descriptive statistics, EFS, and OS analyses. Our study found 19 patients with IKZF1 deletions, with two subjects manifesting more than one deletion. The prevalence in the different subgroups was as follows: Ph/+/ B-ALL 46%, Ph/-/ B-ALL 30%, T-ALL/LBL 4%. There was a statistically significant difference in EFS of 39 vs. 0% in favor of patients without deletions (p = 0.000), which translated to a difference in OS of 49 vs. 0% (p = 0.001). This difference was preserved in the subgroup of Ph/-/ B-ALL, while there was no significant difference in the Ph/+/ B-ALL. The most frequently observed type of deletion (15 out of 19) was the Δ4-7. There is a strong negative prognostic impact of the IKZF1 deletions at diagnosis in the observed population. IKZF1 deletion testing through breakpoint-specific qPCR is a practical approach in diagnostic testing for this risk factor. IKZF1 deletions may warrant treatment decisions and intensified treatment strategies to overcome the negative prognostic impact.

Law of earthquake productivity in the Olami‒Feder‒Christensen‒Zhurkov model

The generalized cellular model based on the of Olami‒Feder‒Christensen cellular automaton model and modified by the allowance for the lifetime of the material on the basis of the kinetic concept for strength of solids by Academician S.N. Zhurkov is used to model and clarify the nature of the statistical law of earthquake productivity. The modified model is named Olami‒Feder‒Christensen‒Zhurkov model (OFCZ). The OFCZ model implements the main statistical regularities of seismicity: the Gutenberg‒Richter and Omori‒Utsu laws, the Bath’s law, fractal geometry of seismicity, and the law of earthquake productivity. It is shown that the clustering of model events (analogs of earthquakes), corresponding to the law of earthquake productivity, is due to the kinetic component of the OFCZ model. The productivity dependences on material strength and medium temperature are obtained. The influence of the Zhurkov parameter and the cell coupling parameter in the cellular model (the dissipativity of the model) on the productivity is considered. It is shown that the revealed dependences of productivity on strength and temperature are consistent with the empirical data.

Poisson-stopped sum Lévy-type processes with application to stochastic modeling of hospital arrivals

Patient arrivals at a hospital typically occur in clusters and the arrivals count data exhibits over-dispersion. To model these characteristics, the Lévy-type processes based on Poisson-stopped sum distributions are proposed as alternatives to the non-homogeneous Poisson process (NHPP), a popular model for hospital arrivals. The arrival rate for the NHPP is a deterministic function of time that does not account for arrivals in clusters while the genesis of Poisson-stopped sum distributions arises from modeling event clusters. Data on daily scheduled patient arrivals over 15-minute intervals collected from a Malaysian public hospital were used to illustrate the application of the Levy-type processes in healthcare management. It is shown that the proposed Lévy-type negative binomial and Lévy-type Thomas processes fit the data better than the NHPP. In addition, the Levy-type processes are computationally simpler and hence, it is envisaged that they will be potentially useful for implementation in staff scheduling and resource allocations.

Development of a Deep Neural Network Model for the Relocation of Mining-Induced Seismic Event

The precise relocation of seismic events is critical for many engineering projects. Swarms of minor or micro earthquakes typically reveal stress concentration and spots of greater seismic hazards. Particularly in the context of deep underground mining, advanced techniques that can accurately relocate microseismicities are urgently in demand. Here, we developed a neural network-based modeling training method that can precisely relocate seismicities and invert for velocities at the same time, with preconfigured receiver network locations. Our model can be iteratively improved with field recorded data. We showed that, with roughly eight iterations, we can reasonably resolve for the earthquake locations for both clusters of events, namely spatially distributed with linear pattern or randomly scattered. Our initially trained model, which only focused on events that had a linear distribution pattern, was used as the base for the training of the subsequent models which could better resolve for randomly scattered event locations. Although we stopped at the eighth iteration, the process reported here can be continued, as the model will have a better performance with more iterations.

Sudden cardiac death after early-onset myocardial infarction: a multicentre longitudinal cohort study with a 20-year follow-up.

Sudden cardiac death (SCD) is a serious consequence of a myocardial infarction (MI), but identifying patients at risk of developing SCD remains a major clinical challenge, especially in the case of juvenile MI. The aim of this study is to identify predictors of SCD after early-onset MI using long-term follow-up data relating to a large nationwide patient cohort. The Italian Genetic Study on Early-onset MI enrolled 2000 patients experiencing a first MI before the age of 45 years, who were followed up for a median of 19.9 years. Fine-Gray proportional hazard models were used to assess the associations between their clinical, demographic, and index event data and the occurrence of SCD. Sudden cardiac death occurred in 195 patients, who were more frequently males, were hypertensive and/or diabetic, had a history of previous thrombo-embolic events with a greater atherosclerotic burden, and had a lower left ventricular ejection fraction (LVEF) after the index event. A multivariable analysis showed that the independent predictors of SCD were diabetes, hypertension, previous thrombo-embolic events, a higher SYNTAX score, and a lower LVEF. There was no clear evidence of the clustering of SCD events during the follow-up. Sudden cardiac death was the first post-MI clinical event in 101 patients; the remaining 94 experienced SCD after a non-fatal MI or hospitalization for coronary revascularization. Sudden cardiac death frequently occurs during the 20 years after early-onset MI. The nature of the identified predictors and the absence of clustering suggest that the pathophysiological basis of SCD may be related to progressive coronary atherosclerosis.

Drought Characterization With GPS: Insights Into Groundwater and Surface‐Reservoir Storage in California

AbstractDrought intensity is commonly characterized using meteorologically‐based metrics that do not provide insight into water deficits within deeper hydrologic systems. In contrast, global positioning system (GPS) displacements are sensitive to both local and regional hydrologic‐storage fluctuations. While a few studies have leveraged this sensitivity to produce geodetic drought indices, hydrologic drought characterization using GPS is not commonly accounted for in drought assessment and management. To motivate this application, we produce a new geodetic drought index (GDI) and quantify its ability to characterize hydrologic drought conditions in key surface and sub‐surface hydrologic reservoirs/pools across California. In northern California, the GDI exhibits a strong regional association with surface‐reservoir storage at the 1‐month time scale (correlation coefficient: 0.83) and groundwater levels at the 3‐month time scale (correlation coefficient: 0.87), along with moderate associations with stream discharge at the daily (instantaneous) time scale (correlation coefficient: 0.50). Groundwater in southern California is best characterized with a 12‐month GDI (correlation coefficient: 0.77), and surface‐reservoir storage is optimized with the 3‐month GDI (correlation coefficient: 0.72). Two sigma uncertainties are ±0.03. Differences between northern and southern California reveal that the GDI is sensitive to unique aquifer and drainage basin characteristics. In addition to capturing long‐term hydrologic trends, rapid changes in the GDI initiate during clusters of large atmospheric river events that closely mirror fluctuations in traditional hydrologic and meteorological observations. We show that GPS‐based hydrologic drought indices provide a significant opportunity to improve drought assessment, in California and beyond, by improving our understanding of the hydrologic cycle.

An unbounded intensity model for point processes

We develop a model for point processes on the real line, where the intensity can be locally unbounded without inducing an explosion. In contrast to an orderly point process, for which the probability of observing more than one event over a short time interval is negligible, the bursting intensity causes an extreme clustering of events around the singularity. We propose a nonparametric approach to detect such bursts in the intensity. It relies on a heavy traffic condition, which admits inference for point processes over a finite time interval. With Monte Carlo evidence, we show that our testing procedure exhibits size control under the null, whereas it has high rejection rates under the alternative. We implement our approach on high-frequency data for the EUR/USD spot exchange rate, where the test statistic captures abnormal surges in trading activity. We detect a nontrivial amount of intensity bursts in these data and describe their basic properties. Trading activity during an intensity burst is positively related to volatility, illiquidity, and the probability of observing a drift burst. The latter effect is reinforced if the order flow is imbalanced or the price elasticity of the limit order book is large.

Temporal scaling theory for bursty time series with clusters of arbitrarily many events.

Long-term temporal correlations in time series in a form of an event sequence have been characterized using an autocorrelation function that often shows a power-law decaying behavior. Such scaling behavior has been mainly accounted for by the heavy-tailed distribution of interevent times, i.e., the time interval between two consecutive events. Yet, little is known about how correlations between consecutive interevent times systematically affect the decaying behavior of the autocorrelation function. Empirical distributions of the burst size, which is the number of events in a cluster of events occurring in a short time window, often show heavy tails, implying that arbitrarily many consecutive interevent times may be correlated with each other. In the present study, we propose a model for generating a time series with arbitrary functional forms of interevent time and burst size distributions. Then, we analytically derive the autocorrelation function for the model time series. In particular, by assuming that the interevent time and burst size are power-law distributed, we derive scaling relations between power-law exponents of the autocorrelation function decay, interevent time distribution, and burst size distribution. These analytical results are confirmed by numerical simulations. Our approach helps to rigorously and analytically understand the effects of correlations between arbitrarily many consecutive interevent times on the decaying behavior of the autocorrelation function.