2,557 research outputs found
Semantically-informed Hierarchical Event Modeling
Prior work has shown that coupling sequential latent variable models with
semantic ontological knowledge can improve the representational capabilities of
event modeling approaches. In this work, we present a novel, doubly
hierarchical, semi-supervised event modeling framework that provides structural
hierarchy while also accounting for ontological hierarchy. Our approach
consists of multiple layers of structured latent variables, where each
successive layer compresses and abstracts the previous layers. We guide this
compression through the injection of structured ontological knowledge that is
defined at the type level of events: importantly, our model allows for partial
injection of semantic knowledge and it does not depend on observing instances
at any particular level of the semantic ontology. Across two different datasets
and four different evaluation metrics, we demonstrate that our approach is able
to out-perform the previous state-of-the-art approaches by up to 8.5%,
demonstrating the benefits of structured and semantic hierarchical knowledge
for event modeling.Comment: Accepted to *SEM 2023. Minor pagination differences in the appendix
due to compiler difference
Adaptive learning for event modeling and pattern classification
It is crucial to detect, characterize and model events of interest in a new propulsion system. As technology advances, the amount of data being generated increases significantly with respect to time. This increase substantially strains our ability to interpret the data at an equivalent rate. It demands efficient methodologies and algorithms in the development of automated event modeling and pattern recognition to detect and characterize events of interest and correlate them to the system performance. The fact that the information required to properly evaluate system performance and health is seldom known in advance further exacerbates this issue.
Event modeling and detection is essentially a discovery problem and involves the use of techniques in the pattern classification domain, specifically the use of cluster analysis if a prior information is unknown. In this dissertation, a framework of Adaptive Learning for Event Modeling and Characterization (ALEC) system is proposed to deal with this problem. Within this framework, a wavelet-based hierarchical fuzzy clustering approach which integrates several advanced technologies and overcomes the disadvantages of traditional clustering algorithms is developed to make the implementation of the system effective and computationally efficient.
In another separate but related research, a generalized multi-dimensional Gaussian membership function is constructed and formulated to make the fuzzy classification of blade engine damage modes among a group of engines containing historical flight data after Principal Component Analysis (PCA) is applied to reduce the excessive dimensionality. This approach can be effectively used to deal with classification of patterns with overlapping structures in which some patterns fall into more than one classes or categories
Simulation Approach to Life-Data Queue Event Modeling
Simulation process provides a platform to model the real-life scenario from an experimental viewpoint. Simulation plays a key role in providing output that could be used to model the real-life. Queue patterns are studies from two perspective: (1) the stochastic method and (2) simulation technique. This paper spurs on discrete event simulation (DES) technique to investigate the assertions already made in queue model research works. Life-Data already collected from Johnson et al (2018) was used and simulation carried out using simmer package in R language. Findings validate the result of assertions made some research literatures. Keywords: Simulation, Queue, Discrete, Simmer, assertions DOI: 10.7176/CTI/8-0
A Relational Event Approach to Modeling Behavioral Dynamics
This chapter provides an introduction to the analysis of relational event
data (i.e., actions, interactions, or other events involving multiple actors
that occur over time) within the R/statnet platform. We begin by reviewing the
basics of relational event modeling, with an emphasis on models with piecewise
constant hazards. We then discuss estimation for dyadic and more general
relational event models using the relevent package, with an emphasis on
hands-on applications of the methods and interpretation of results. Statnet is
a collection of packages for the R statistical computing system that supports
the representation, manipulation, visualization, modeling, simulation, and
analysis of relational data. Statnet packages are contributed by a team of
volunteer developers, and are made freely available under the GNU Public
License. These packages are written for the R statistical computing
environment, and can be used with any computing platform that supports R
(including Windows, Linux, and Mac).
Dynamic Modeling and Statistical Analysis of Event Times
This review article provides an overview of recent work in the modeling and
analysis of recurrent events arising in engineering, reliability, public
health, biomedicine and other areas. Recurrent event modeling possesses unique
facets making it different and more difficult to handle than single event
settings. For instance, the impact of an increasing number of event occurrences
needs to be taken into account, the effects of covariates should be considered,
potential association among the interevent times within a unit cannot be
ignored, and the effects of performed interventions after each event occurrence
need to be factored in. A recent general class of models for recurrent events
which simultaneously accommodates these aspects is described. Statistical
inference methods for this class of models are presented and illustrated
through applications to real data sets. Some existing open research problems
are described.Comment: Published at http://dx.doi.org/10.1214/088342306000000349 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Self-Supervised Time-to-Event Modeling with Structured Medical Records
Time-to-event (TTE) models are used in medicine and other fields for
estimating the probability distribution of the time until a specific event
occurs. TTE models provide many advantages over classification using fixed time
horizons, including naturally handling censored observations, but require more
parameters and are challenging to train in settings with limited labeled data.
Existing approaches, e.g. proportional hazards or accelerated failure time,
employ distributional assumptions to reduce parameters but are vulnerable to
model misspecification. In this work, we address these challenges with MOTOR
(Many Outcome Time Oriented Representations), a self-supervised model that
leverages temporal structure found in collections of timestamped events in
electronic health records (EHR) and health insurance claims. MOTOR uses a TTE
pretraining objective that predicts the probability distribution of times when
events occur, making it well-suited to transfer learning for medical prediction
tasks. Having pretrained on EHR and claims data of up to 55M patient records
(9B clinical events), we evaluate performance after finetuning for 19 tasks
across two datasets. Task-specific models built using MOTOR improve
time-dependent C statistics by 4.6% over state-of-the-art while greatly
improving sample efficiency, achieving comparable performance to existing
methods using only 5% of available task data
Bayesian Point Event Modeling in Spatial and Environmental Epidemiology: A Review
This paper reviews the current state of point event modeling in spatial epidemiology from a Bayesian perspective. Point event (or case event) data arise when geo-coded addresses of disease events are available. Often this level of spatial resolution would not be accessible due to medical confidentiality constraints. However, for the examination of small spatial scales it is important to be capable of examining point process data directly. Models for such data are usually formulated based on point process theory. In addition, special conditioning arguments can lead to simpler Bernoulli likelihoods and logistic spatial models. Goodness-of-fit diagnostics and Bayesian residuals are also considered. Applications within putative health hazard risk assessment, cluster detection, and linkage to environmental risk fields (misalignment) are considered
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