571 research outputs found
Straight to Shapes: Real-time Detection of Encoded Shapes
Current object detection approaches predict bounding boxes, but these provide
little instance-specific information beyond location, scale and aspect ratio.
In this work, we propose to directly regress to objects' shapes in addition to
their bounding boxes and categories. It is crucial to find an appropriate shape
representation that is compact and decodable, and in which objects can be
compared for higher-order concepts such as view similarity, pose variation and
occlusion. To achieve this, we use a denoising convolutional auto-encoder to
establish an embedding space, and place the decoder after a fast end-to-end
network trained to regress directly to the encoded shape vectors. This yields
what to the best of our knowledge is the first real-time shape prediction
network, running at ~35 FPS on a high-end desktop. With higher-order shape
reasoning well-integrated into the network pipeline, the network shows the
useful practical quality of generalising to unseen categories similar to the
ones in the training set, something that most existing approaches fail to
handle.Comment: 16 pages including appendix; Published at CVPR 201
Anomaly detection and virtual reality visualisation in supercomputers
Anomaly detection is the identification of events or observations that deviate from the expected behaviour of a given set of data. Its main application is the prediction of possible technical failures. In particular, anomaly detection on supercomputers is a difficult problem to solve due to the large scale of the systems and the large number of components. Most research works in this field employ machine learning methods and regression models in a supervised fashion, which implies the need for a large amount of labelled data to train such systems. This work proposes the use of autoencoder models, allowing the problem to be approached with semi-supervised learning techniques. Two different model training approaches are compared. The former is a model trained with data from all the nodes of a supercomputer. In the latter approach, observing significant differences between nodes, one model is trained for each node. The results are analysed by evaluating the positive and negative aspects of each approach. On the other hand, a replica of the Marconi 100 supercomputer is developed in a virtual reality environment that allows the data from each node to be visualised at the same time.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. We would like to thank “A way of making Europe” European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 for supporting this work under the MoDeaAS project (grant PID2019-104818RB-I00). Furthermore, we would like to thank the University of Skövde and to ASSAR Innovation Arena for their support to develop this work
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data
The ability to perform accurate prognosis of patients is crucial for
proactive clinical decision making, informed resource management and
personalised care. Existing outcome prediction models suffer from a low recall
of infrequent positive outcomes. We present a highly-scalable and robust
machine learning framework to automatically predict adversity represented by
mortality and ICU admission from time-series vital signs and laboratory results
obtained within the first 24 hours of hospital admission. The stacked platform
comprises two components: a) an unsupervised LSTM Autoencoder that learns an
optimal representation of the time-series, using it to differentiate the less
frequent patterns which conclude with an adverse event from the majority
patterns that do not, and b) a gradient boosting model, which relies on the
constructed representation to refine prediction, incorporating static features
of demographics, admission details and clinical summaries. The model is used to
assess a patient's risk of adversity over time and provides visual
justifications of its prediction based on the patient's static features and
dynamic signals. Results of three case studies for predicting mortality and ICU
admission show that the model outperforms all existing outcome prediction
models, achieving PR-AUC of 0.891 (95 CI: 0.878 - 0.969) in predicting
mortality in ICU and general ward settings and 0.908 (95 CI: 0.870-0.935) in
predicting ICU admission.Comment: 14 page
A Survey on Explainable Anomaly Detection
In the past two decades, most research on anomaly detection has focused on
improving the accuracy of the detection, while largely ignoring the
explainability of the corresponding methods and thus leaving the explanation of
outcomes to practitioners. As anomaly detection algorithms are increasingly
used in safety-critical domains, providing explanations for the high-stakes
decisions made in those domains has become an ethical and regulatory
requirement. Therefore, this work provides a comprehensive and structured
survey on state-of-the-art explainable anomaly detection techniques. We propose
a taxonomy based on the main aspects that characterize each explainable anomaly
detection technique, aiming to help practitioners and researchers find the
explainable anomaly detection method that best suits their needs.Comment: Paper accepted by the ACM Transactions on Knowledge Discovery from
Data (TKDD) for publication (preprint version
Immersive analytics for oncology patient cohorts
This thesis proposes a novel interactive immersive analytics tool and methods to interrogate the cancer patient cohort in an immersive virtual environment, namely Virtual Reality to Observe Oncology data Models (VROOM). The overall objective is to develop an immersive analytics platform, which includes a data analytics pipeline from raw gene expression data to immersive visualisation on virtual and augmented reality platforms utilising a game engine. Unity3D has been used to implement the visualisation. Work in this thesis could provide oncologists and clinicians with an interactive visualisation and visual analytics platform that helps them to drive their analysis in treatment efficacy and achieve the goal of evidence-based personalised medicine. The thesis integrates the latest discovery and development in cancer patients’ prognoses, immersive technologies, machine learning, decision support system and interactive visualisation to form an immersive analytics platform of complex genomic data. For this thesis, the experimental paradigm that will be followed is in understanding transcriptomics in cancer samples. This thesis specifically investigates gene expression data to determine the biological similarity revealed by the patient's tumour samples' transcriptomic profiles revealing the active genes in different patients. In summary, the thesis contributes to i) a novel immersive analytics platform for patient cohort data interrogation in similarity space where the similarity space is based on the patient's biological and genomic similarity; ii) an effective immersive environment optimisation design based on the usability study of exocentric and egocentric visualisation, audio and sound design optimisation; iii) an integration of trusted and familiar 2D biomedical visual analytics methods into the immersive environment; iv) novel use of the game theory as the decision-making system engine to help the analytics process, and application of the optimal transport theory in missing data imputation to ensure the preservation of data distribution; and v) case studies to showcase the real-world application of the visualisation and its effectiveness
Rapid Spectral Parameter Prediction for Black Hole X-Ray Binaries using Physicalised Autoencoders
Black hole X-ray binaries (BHBs) offer insights into extreme gravitational
environments and the testing of general relativity. The X-ray spectrum
collected by NICER offers valuable information on the properties and behaviour
of BHBs through spectral fitting. However, traditional spectral fitting methods
are slow and scale poorly with model complexity. This paper presents a new
semi-supervised autoencoder neural network for parameter prediction and
spectral reconstruction of BHBs, showing an improvement of up to a factor of
2,700 in speed while maintaining comparable accuracy. The approach maps the
spectral features from the numerous outbursts catalogued by NICER and
generalises them to new systems for efficient and accurate spectral fitting.
The effectiveness of this approach is demonstrated in the spectral fitting of
BHBs and holds promise for use in other areas of astronomy and physics for
categorising large datasets.Comment: 12 pages, 12 figure
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