14,409 research outputs found
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
Online Network Source Optimization with Graph-Kernel MAB
We propose Grab-UCB, a graph-kernel multi-arms bandit algorithm to learn
online the optimal source placement in large scale networks, such that the
reward obtained from a priori unknown network processes is maximized. The
uncertainty calls for online learning, which suffers however from the curse of
dimensionality. To achieve sample efficiency, we describe the network processes
with an adaptive graph dictionary model, which typically leads to sparse
spectral representations. This enables a data-efficient learning framework,
whose learning rate scales with the dimension of the spectral representation
model instead of the one of the network. We then propose Grab-UCB, an online
sequential decision strategy that learns the parameters of the spectral
representation while optimizing the action strategy. We derive the performance
guarantees that depend on network parameters, which further influence the
learning curve of the sequential decision strategy We introduce a
computationally simplified solving method, Grab-arm-Light, an algorithm that
walks along the edges of the polytope representing the objective function.
Simulations results show that the proposed online learning algorithm
outperforms baseline offline methods that typically separate the learning phase
from the testing one. The results confirm the theoretical findings, and further
highlight the gain of the proposed online learning strategy in terms of
cumulative regret, sample efficiency and computational complexity
Segment Anything Model (SAM) for Radiation Oncology
In this study, we evaluate the performance of the Segment Anything Model
(SAM) model in clinical radiotherapy. We collected real clinical cases from
four regions at the Mayo Clinic: prostate, lung, gastrointestinal, and head \&
neck, which are typical treatment sites in radiation oncology. For each case,
we selected the OARs of concern in radiotherapy planning and compared the Dice
and Jaccard outcomes between clinical manual delineation, automatic
segmentation using SAM's "segment anything" mode, and automatic segmentation
using SAM with box prompt. Our results indicate that SAM performs better in
automatic segmentation for the prostate and lung regions, while its performance
in the gastrointestinal and head \& neck regions was relatively inferior. When
considering the size of the organ and the clarity of its boundary, SAM displays
better performance for larger organs with clear boundaries, such as the lung
and liver, and worse for smaller organs with unclear boundaries, like the
parotid and cochlea. These findings align with the generally accepted
variations in difficulty level associated with manual delineation of different
organs at different sites in clinical radiotherapy. Given that SAM, a single
trained model, could handle the delineation of OARs in four regions, these
results also demonstrate SAM's robust generalization capabilities in automatic
segmentation for radiotherapy, i.e., achieving delineation of different
radiotherapy OARs using a generic automatic segmentation model. SAM's
generalization capabilities across different regions make it technically
feasible to develop a generic model for automatic segmentation in radiotherapy
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Machine learning approach towards predicting turbulent fluid flow using convolutional neural networks
Using convolutional neural networks, we present a novel method for predicting turbulent fluid flow through an array of obstacles in this thesis. In recent years, machine learning has exploded in popularity due to its ability to create accurate data driven models and the abundance of available data. In an attempt to understand the characteristics of turbulent fluid flow, we utilise a novel convolutional autoencoder neural network to predict the first ten POD modes of turbulent fluid flow. We find
that the model is able to predict the first two POD modes well although and with less accuracy for the remaining eight POD modes. In addition, we find that the
ML-predicted POD modes are accurate enough to be used to reconstruct turbulent flow that adequately captures the large-scale details of the original simulation
Generalizable deep learning based medical image segmentation
Deep learning is revolutionizing medical image analysis and interpretation. However, its real-world deployment is often hindered by the poor generalization to unseen domains (new imaging modalities and protocols). This lack of generalization ability is further exacerbated by the scarcity of labeled datasets for training: Data collection and annotation can be prohibitively expensive in terms of labor and costs because label quality heavily dependents on the expertise of radiologists. Additionally, unreliable predictions caused by poor model generalization pose safety risks to clinical downstream applications.
To mitigate labeling requirements, we investigate and develop a series of techniques to strengthen the generalization ability and the data efficiency of deep medical image computing models. We further improve model accountability and identify unreliable predictions made on out-of-domain data, by designing probability calibration techniques.
In the first and the second part of thesis, we discuss two types of problems for handling unexpected domains: unsupervised domain adaptation and single-source domain generalization. For domain adaptation we present a data-efficient technique that adapts a segmentation model trained on a labeled source domain (e.g., MRI) to an unlabeled target domain (e.g., CT), using a small number of unlabeled training images from the target domain.
For domain generalization, we focus on both image reconstruction and segmentation. For image reconstruction, we design a simple and effective domain generalization technique for cross-domain MRI reconstruction, by reusing image representations learned from natural image datasets. For image segmentation, we perform causal analysis of the challenging cross-domain image segmentation problem. Guided by this causal analysis we propose an effective data-augmentation-based generalization technique for single-source domains. The proposed method outperforms existing approaches on a large variety of cross-domain image segmentation scenarios.
In the third part of the thesis, we present a novel self-supervised method for learning generic image representations that can be used to analyze unexpected objects of interest. The proposed method is designed together with a novel few-shot image segmentation framework that can segment unseen objects of interest by taking only a few labeled examples as references. Superior flexibility over conventional fully-supervised models is demonstrated by our few-shot framework: it does not require any fine-tuning on novel objects of interest. We further build a publicly available comprehensive evaluation environment for few-shot medical image segmentation.
In the fourth part of the thesis, we present a novel probability calibration model. To ensure safety in clinical settings, a deep model is expected to be able to alert human radiologists if it has low confidence, especially when confronted with out-of-domain data. To this end we present a plug-and-play model to calibrate prediction probabilities on out-of-domain data. It aligns the prediction probability in line with the actual accuracy on the test data. We evaluate our method on both artifact-corrupted images and images from an unforeseen MRI scanning protocol. Our method demonstrates improved calibration accuracy compared with the state-of-the-art method.
Finally, we summarize the major contributions and limitations of our works. We also suggest future research directions that will benefit from the works in this thesis.Open Acces
AlerTiger: Deep Learning for AI Model Health Monitoring at LinkedIn
Data-driven companies use AI models extensively to develop products and
intelligent business solutions, making the health of these models crucial for
business success. Model monitoring and alerting in industries pose unique
challenges, including a lack of clear model health metrics definition, label
sparsity, and fast model iterations that result in short-lived models and
features. As a product, there are also requirements for scalability,
generalizability, and explainability. To tackle these challenges, we propose
AlerTiger, a deep-learning-based MLOps model monitoring system that helps AI
teams across the company monitor their AI models' health by detecting anomalies
in models' input features and output score over time. The system consists of
four major steps: model statistics generation, deep-learning-based anomaly
detection, anomaly post-processing, and user alerting. Our solution generates
three categories of statistics to indicate AI model health, offers a two-stage
deep anomaly detection solution to address label sparsity and attain the
generalizability of monitoring new models, and provides holistic reports for
actionable alerts. This approach has been deployed to most of LinkedIn's
production AI models for over a year and has identified several model issues
that later led to significant business metric gains after fixing
A Type-2 Fuzzy Based Explainable AI System for Predictive Maintenance within the Water Pumping Industry
Industrial maintenance has undergone a paradigm shift due to the emergence of artificial intelligence (AI), the Internet of Things (IoT), and cloud computing. Rather than accepting the drawbacks of reactive maintenance, leading firms worldwide are embracing "predict-and-prevent" maintenance. However, opaque box AI models are sophisticated and complex for the average user to comprehend and explain. This limits the AI employment in predictive maintenance, where it is vital to understand and evaluate the model before deployment. In addition, it's also important to comprehend the maintenance system's decisions. This paper presents a type-2 fuzzy-based Explainable AI (XAI) system for predictive maintenance within the water pumping industry. The proposed system is optimised via Big-Bang Big-Crunch (BB-BC), which maximises the model accuracy for predicting faults while maximising model interpretability. We evaluated the proposed system on water pumps using real-time data obtained by our hardware placed at real-world locations around the United Kingdom and compared our model with Type-1 Fuzzy Logic System (T1FLS), a Multi-Layer Perceptron (MLP) Neural Network, an effective deep learning method known as stacked autoencoders (SAEs) and an interpretable model like decision trees (DT). The proposed system predicted water pumping equipment failures with good accuracy (outperforming the T1FLS accuracy by 8.9% and DT by 529.2% while providing comparable results to SAEs and MLPs) and interpretability. The system predictions comprehend why a specific problem may occur, which leads to better and more informed customer visits to reduce equipment failure disturbances. It will be shown that 80.3% of water industry specialists strongly agree with the model's explanation, determining its acceptance
A CNN-Based Novel Approach for Classification of Sacral Hiatus with GAN-Powered Tabular Data Set
Caudal epidural anaesthesia is usually the most well-known technique in obstetrics to deal with chronic back pain. Due to variations in the shape and size of the sacral hiatus (SH), its classification is a crucial and challenging task. Clinically, it is required in trauma, where surgeons must make fast and correct selections. Past studies have focused on morphometric and statistical analysis to classify it. Therefore, it is vital to automatically and accurately classify SH types through deep learning methods. To this end, we proposed the Multi-Task Process (MTP), a novel classification approach to classify the SH MTP that initially uses a small medical tabular data set obtained by manual feature extraction on computed tomography scans of the sacrums. Second, it augments the data set synthetically through a Generative Adversarial Network (GAN). In addition, it adapts a two-dimensional (2D) embedding algorithm to convert tabular features into images. Finally, it feeds images into Convolutional Neural Networks (CNNs). The application of MTP to six CNN models achieved remarkable classification success rates of approximately 90Â % to 93Â %. The proposed MTP approach eliminates the small medical tabular data problem that results in bone classification on deep models
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