16,511 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
The infrared structure of perturbative gauge theories
Infrared divergences in the perturbative expansion of gauge theory amplitudes and cross sections have been a focus of theoretical investigations for almost a century. New insights still continue to emerge, as higher perturbative orders are explored, and high-precision phenomenological applications demand an ever more refined understanding. This review aims to provide a pedagogical overview of the subject. We briefly cover some of the early historical results, we provide some simple examples of low-order applications in the context of perturbative QCD, and discuss the necessary tools to extend these results to all perturbative orders. Finally, we describe recent developments concerning the calculation of soft anomalous dimensions in multi-particle scattering amplitudes at high orders, and we provide a brief introduction to the very active field of infrared subtraction for the calculation of differential distributions at colliders. © 2022 Elsevier B.V
Time-vs. frequency-domain inverse elastic scattering: Theory and experiment
International audienceThis study formally adapts the time-domain linear sampling method (TLSM) for ultrasonic imaging of stationary and evolving fractures in safety-critical components. The TLSM indicator is then applied to the laboratory test data of [22, 18] and the obtained reconstructions are compared to their frequency-domain counterparts. The results highlight the unique capability of the time-domain imaging functional for high-fidelity tracking of evolving damage, and its relative robustness to sparse and reduced-aperture data at moderate noise levels. A comparative analysis of the TLSM images against the multifrequency LSM maps of [22] further reveals that thanks to the full-waveform inversion in time and space, the TLSM generates images of remarkably higher quality with the same dataset
Machine learning in solar physics
The application of machine learning in solar physics has the potential to
greatly enhance our understanding of the complex processes that take place in
the atmosphere of the Sun. By using techniques such as deep learning, we are
now in the position to analyze large amounts of data from solar observations
and identify patterns and trends that may not have been apparent using
traditional methods. This can help us improve our understanding of explosive
events like solar flares, which can have a strong effect on the Earth
environment. Predicting hazardous events on Earth becomes crucial for our
technological society. Machine learning can also improve our understanding of
the inner workings of the sun itself by allowing us to go deeper into the data
and to propose more complex models to explain them. Additionally, the use of
machine learning can help to automate the analysis of solar data, reducing the
need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a
Living Review in Solar Physics (LRSP
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport
Recently, Neural Topic Models (NTM), inspired by variational autoencoders,
have attracted a lot of research interest; however, these methods have limited
applications in the real world due to the challenge of incorporating human
knowledge. This work presents a semi-supervised neural topic modeling method,
vONTSS, which uses von Mises-Fisher (vMF) based variational autoencoders and
optimal transport. When a few keywords per topic are provided, vONTSS in the
semi-supervised setting generates potential topics and optimizes topic-keyword
quality and topic classification. Experiments show that vONTSS outperforms
existing semi-supervised topic modeling methods in classification accuracy and
diversity. vONTSS also supports unsupervised topic modeling. Quantitative and
qualitative experiments show that vONTSS in the unsupervised setting
outperforms recent NTMs on multiple aspects: vONTSS discovers highly clustered
and coherent topics on benchmark datasets. It is also much faster than the
state-of-the-art weakly supervised text classification method while achieving
similar classification performance. We further prove the equivalence of optimal
transport loss and cross-entropy loss at the global minimum.Comment: 24 pages, 12 figures, ACL findings 202
Networked Time Series Prediction with Incomplete Data
A networked time series (NETS) is a family of time series on a given graph,
one for each node. It has a wide range of applications from intelligent
transportation, environment monitoring to smart grid management. An important
task in such applications is to predict the future values of a NETS based on
its historical values and the underlying graph. Most existing methods require
complete data for training. However, in real-world scenarios, it is not
uncommon to have missing data due to sensor malfunction, incomplete sensing
coverage, etc. In this paper, we study the problem of NETS prediction with
incomplete data. We propose NETS-ImpGAN, a novel deep learning framework that
can be trained on incomplete data with missing values in both history and
future. Furthermore, we propose Graph Temporal Attention Networks, which
incorporate the attention mechanism to capture both inter-time series and
temporal correlations. We conduct extensive experiments on four real-world
datasets under different missing patterns and missing rates. The experimental
results show that NETS-ImpGAN outperforms existing methods, reducing the MAE by
up to 25%
CAR-DESPOT: Causally-Informed Online POMDP Planning for Robots in Confounded Environments
Robots operating in real-world environments must reason about possible
outcomes of stochastic actions and make decisions based on partial observations
of the true world state. A major challenge for making accurate and robust
action predictions is the problem of confounding, which if left untreated can
lead to prediction errors. The partially observable Markov decision process
(POMDP) is a widely-used framework to model these stochastic and
partially-observable decision-making problems. However, due to a lack of
explicit causal semantics, POMDP planning methods are prone to confounding bias
and thus in the presence of unobserved confounders may produce underperforming
policies. This paper presents a novel causally-informed extension of "anytime
regularized determinized sparse partially observable tree" (AR-DESPOT), a
modern anytime online POMDP planner, using causal modelling and inference to
eliminate errors caused by unmeasured confounder variables. We further propose
a method to learn offline the partial parameterisation of the causal model for
planning, from ground truth model data. We evaluate our methods on a toy
problem with an unobserved confounder and show that the learned causal model is
highly accurate, while our planning method is more robust to confounding and
produces overall higher performing policies than AR-DESPOT.Comment: 8 pages, 3 figures, submitted to 2023 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS
Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models
This paper considers the specification of covariance structures with tail
estimates. We focus on two aspects: (i) the estimation of the VaR-CoVaR risk
matrix in the case of larger number of time series observations than assets in
a portfolio using quantile predictive regression models without assuming the
presence of nonstationary regressors and; (ii) the construction of a novel
variable selection algorithm, so-called, Feature Ordering by Centrality
Exclusion (FOCE), which is based on an assumption-lean regression framework,
has no tuning parameters and is proved to be consistent under general sparsity
assumptions. We illustrate the usefulness of our proposed methodology with
numerical studies of real and simulated datasets when modelling systemic risk
in a network
Large-Scale Study of Temporal Shift in Health Insurance Claims
Most machine learning models for predicting clinical outcomes are developed
using historical data. Yet, even if these models are deployed in the near
future, dataset shift over time may result in less than ideal performance. To
capture this phenomenon, we consider a task--that is, an outcome to be
predicted at a particular time point--to be non-stationary if a historical
model is no longer optimal for predicting that outcome. We build an algorithm
to test for temporal shift either at the population level or within a
discovered sub-population. Then, we construct a meta-algorithm to perform a
retrospective scan for temporal shift on a large collection of tasks. Our
algorithms enable us to perform the first comprehensive evaluation of temporal
shift in healthcare to our knowledge. We create 1,010 tasks by evaluating 242
healthcare outcomes for temporal shift from 2015 to 2020 on a health insurance
claims dataset. 9.7% of the tasks show temporal shifts at the population level,
and 93.0% have some sub-population affected by shifts. We dive into case
studies to understand the clinical implications. Our analysis highlights the
widespread prevalence of temporal shifts in healthcare.Comment: To appear as an oral spotlight and poster at Conference on Health,
Inference, and Learning (CHIL) 202
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