484 research outputs found
Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services
This study develops an online predictive optimization framework for
dynamically operating a transit service in an area of crowd movements. The
proposed framework integrates demand prediction and supply optimization to
periodically redesign the service routes based on recently observed demand. To
predict demand for the service, we use Quantile Regression to estimate the
marginal distribution of movement counts between each pair of serviced
locations. The framework then combines these marginals into a joint demand
distribution by constructing a Gaussian copula, which captures the structure of
correlation between the marginals. For supply optimization, we devise a linear
programming model, which simultaneously determines the route structure and the
service frequency according to the predicted demand. Importantly, our framework
both preserves the uncertainty structure of future demand and leverages this
for robust route optimization, while keeping both components decoupled. We
evaluate our framework using a real-world case study of autonomous mobility in
a university campus in Denmark. The results show that our framework often
obtains the ground truth optimal solution, and can outperform conventional
methods for route optimization, which do not leverage full predictive
distributions.Comment: 34 pages, 12 figures, 5 table
Efficient Variational Bayesian Structure Learning of Dynamic Graphical Models
Estimating time-varying graphical models are of paramount importance in
various social, financial, biological, and engineering systems, since the
evolution of such networks can be utilized for example to spot trends, detect
anomalies, predict vulnerability, and evaluate the impact of interventions.
Existing methods require extensive tuning of parameters that control the graph
sparsity and temporal smoothness. Furthermore, these methods are
computationally burdensome with time complexity O(NP^3) for P variables and N
time points. As a remedy, we propose a low-complexity tuning-free Bayesian
approach, named BADGE. Specifically, we impose temporally-dependent
spike-and-slab priors on the graphs such that they are sparse and varying
smoothly across time. A variational inference algorithm is then derived to
learn the graph structures from the data automatically. Owning to the
pseudo-likelihood and the mean-field approximation, the time complexity of
BADGE is only O(NP^2). Additionally, by identifying the frequency-domain
resemblance to the time-varying graphical models, we show that BADGE can be
extended to learning frequency-varying inverse spectral density matrices, and
yields graphical models for multivariate stationary time series. Numerical
results on both synthetic and real data show that that BADGE can better recover
the underlying true graphs, while being more efficient than the existing
methods, especially for high-dimensional cases
Transformer Convolutional Neural Networks for Automated Artifact Detection in Scalp EEG
It is well known that electroencephalograms (EEGs) often contain artifacts
due to muscle activity, eye blinks, and various other causes. Detecting such
artifacts is an essential first step toward a correct interpretation of EEGs.
Although much effort has been devoted to semi-automated and automated artifact
detection in EEG, the problem of artifact detection remains challenging. In
this paper, we propose a convolutional neural network (CNN) enhanced by
transformers using belief matching (BM) loss for automated detection of five
types of artifacts: chewing, electrode pop, eye movement, muscle, and shiver.
Specifically, we apply these five detectors at individual EEG channels to
distinguish artifacts from background EEG. Next, for each of these five types
of artifacts, we combine the output of these channel-wise detectors to detect
artifacts in multi-channel EEG segments. These segment-level classifiers can
detect specific artifacts with a balanced accuracy (BAC) of 0.947, 0.735,
0.826, 0.857, and 0.655 for chewing, electrode pop, eye movement, muscle, and
shiver artifacts, respectively. Finally, we combine the outputs of the five
segment-level detectors to perform a combined binary classification (any
artifact vs. background). The resulting detector achieves a sensitivity (SEN)
of 60.4%, 51.8%, and 35.5%, at a specificity (SPE) of 95%, 97%, and 99%,
respectively. This artifact detection module can reject artifact segments while
only removing a small fraction of the background EEG, leading to a cleaner EEG
for further analysis.Comment: This is an extension to a paper presented at the 2022 44th Annual
International Conference of the IEEE Engineering in Medicine & Biology
Society (EMBC) Scottish Event Campus, Glasgow, UK, July 11-15, 202
Alternative Techniques of Neural Signal Processing in Neuroengineering
Neural signal processing is a discipline within neuroengineering. This interdisciplinary approach combines principles from machine learning, signal processing theory, and computational neuroscience applied to problems in basic and clinical neuroscience. The ultimate goal of neuroengineering is a technological revolution, where machines would interact in real time with the brain. Machines and brains could interface, enabling normal function in cases of injury or disease, brain monitoring, and/or medical rehabilitation of brain disorders.
Much current research in neuroengineering is focused on understanding the coding and processing of information in the sensory and motor systems, quantifying how this processing is altered in the pathological state, and how it can be manipulated through interactions with artificial devices including brain–computer interfaces and neuroprosthetics
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