20,457 research outputs found
Image classification over unknown and anomalous domains
A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting.
Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each.
While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so.
In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks
Exploring the effects of spinal cord stimulation for freezing of gait in parkinsonian patients
Dopaminergic replacement therapies (e.g. levodopa) provide limited to no response for axial motor symptoms including gait dysfunction and freezing of gait (FOG) in Parkinson’s disease (PD) and Richardson’s syndrome progressive supranuclear palsy (PSP-RS) patients. Dopaminergic-resistant FOG may be a sensorimotor processing issue that does not involve basal ganglia (nigrostriatal) impairment. Recent studies suggest that spinal cord stimulation (SCS) has positive yet variable effects for dopaminergic-resistant gait and FOG in parkinsonian patients. Further studies investigating the mechanism of SCS, optimal stimulation parameters, and longevity of effects for alleviating FOG are warranted. The hypothesis of the research described in this thesis is that mid-thoracic, dorsal SCS effectively reduces FOG by modulating the sensory processing system in gait and may have a dopaminergic effect in individuals with FOG. The primary objective was to understand the relationship between FOG reduction, improvements in upper limb visual-motor performance, modulation of cortical activity and striatal dopaminergic innervation in 7 PD participants. FOG reduction was associated with changes in upper limb reaction time, speed and accuracy measured using robotic target reaching choice tasks. Modulation of resting-state, sensorimotor cortical activity, recorded using electroencephalography, was significantly associated with FOG reduction while participants were OFF-levodopa. Thus, SCS may alleviate FOG by modulating cortical activity associated with motor planning and sensory perception. Changes to striatal dopaminergic innervation, measured using a dopamine transporter marker, were associated with visual-motor performance improvements. Axial and appendicular motor features may be mediated by non-dopaminergic and dopaminergic pathways, respectively. The secondary objective was to demonstrate the short- and long-term effects of SCS for alleviating dopaminergic-resistant FOG and gait dysfunction in 5 PD and 3 PSP-RS participants without back/leg pain. SCS programming was individualized based on which setting best improved gait and/or FOG responses per participant using objective gait analysis. Significant improvements in stride velocity, step length and reduced FOG frequency were observed in all PD participants with up to 3-years of SCS. Similar gait and FOG improvements were observed in all PSP-RS participants up to 6-months. SCS is a promising therapeutic option for parkinsonian patients with FOG by possibly influencing cortical and subcortical structures involved in locomotion physiology
Comparative study of classification algorithms for quality assessment of resistance spot welding joints from preand post-welding inputs
Resistance spot welding (RSW) is a widespread manufacturing process in the automotive industry. There are different approaches for assessing the quality level of RSW joints. Multi-input-single-output methods, which take as inputs either the intrinsic parameters of the welding process or ultrasonic nondestructive testing variables, are commonly used. This work demonstrates that the combined use of both types of inputs can significantly improve the already competitive approach based exclusively on ultrasonic analyses. The use of stacking of tree ensemble models as classifiers dominates the classification results in terms of accuracy, F-measure and area under the receiver operating characteristic curve metrics. Through variable importance analyses, the results show that although the welding process parameters are less relevant than the ultrasonic testing variables, some of the former provide marginal information not fully captured by the latter
BECOMEBECOME - A TRANSDISCIPLINARY METHODOLOGY BASED ON INFORMATION ABOUT THE OBSERVER
ABSTRACT
Andrea T. R. Traldi
BECOMEBECOME
A Transdisciplinary Methodology Based on Information about the Observer
The present research dissertation has been developed with the intention to provide practical strategies and discover new intellectual operations which can be used to generate Transdisciplinary insight. For this reason, this thesis creates access to new knowledge at different scales.
Firstly, as it pertains to the scale of new knowledge generated by those who attend Becomebecome events. The open-source nature of the Becomebecome methodology makes it possible for participants in Becomebecome workshops, training programmes and residencies to generate new insight about the specific project they are working on, which then reinforce and expand the foundational principles of the theoretical background.
Secondly, as it pertains to the scale of the Becomebecome framework, which remains independent of location and moment in time. The method proposed to access Transdisciplinary knowledge constitutes new knowledge in itself because the sequence of activities, described as physical and mental procedures and listed as essential criteria, have never been found organised
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in such a specific order before. It is indeed the order in time, i.e. the sequence of the ideas and activities proposed, which allows one to transform Disciplinary knowledge via a new Transdisciplinary frame of reference.
Lastly, new knowledge about Transdisciplinarity as a field of study is created as a consequence of the heretofore listed two processes.
The first part of the thesis is designated ‘Becomebecome Theory’ and focuses on the theoretical background and the intellectual operations necessary to support the creation of new Transdisciplinary knowledge. The second part of the thesis is designated ‘Becomebecome Practice’ and provides practical examples of the application of such operations. Crucially, the theoretical model described as the foundation for the Becomebecome methodology (Becomebecome Theory) is process-based and constantly checked against the insight generated through Becomebecome Practice.
To this effect, ‘information about the observer’ is proposed as a key notion which binds together Transdisciplinary resources from several studies in the hard sciences and humanities. It is a concept that enables understanding about why and how information that is generated through Becomebecome Practice is considered of paramount importance for establishing the reference parameters necessary to access Transdisciplinary insight which is meaningful to a specific project, a specific person, or a specific moment in time
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Quantitative Character and the Composite Account of Phenomenal Content
I advance an account of quantitative character, a species of phenomenal character that presents as an intensity (cf. a quality) and includes experience dimensions such as loudness, pain intensity, and visual pop-out. I employ psychological and neuroscientific evidence to demonstrate that quantitative characters are best explained by attentional processing, and hence that they do not represent external qualities. Nonetheless, the proposed account of quantitative character is conceived as a compliment to the reductive intentionalist strategy toward qualitative states; I argue that an account of perceptual experience that combines a tracking account of qualitative character with my functionalist proposal of quantitative character permits replies to some notoriously difficult problems for tracking representationalism without sacrificing its chief virtues
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NOAH-H, a deep-learning, terrain classification system for Mars: Results for the ExoMars Rover candidate landing sites
In this investigation a deep learning terrain classification system, the “Novelty or Anomaly Hunter – HiRISE” (NOAH-H), was used to classify High Resolution Imaging Science Experiment (HiRISE) images of Oxia Planum and Mawrth Vallis. A set of ontological classes was developed that covered the variety of surface textures and aeolian bedforms present at both sites. Labelled type-examples of these classes were used to train a Deep Neural Network (DNN) to perform semantic segmentation in order to identify these classes in further HiRISE images.
This contribution discusses the methods and results of the study from a geomorphologists perspective, providing a case study applying machine learning to a landscape classification task. Our aim is to highlight considerations about how to compile training datasets, select ontological classes, and understand what such systems can and cannot do. We highlight issues that arise when adapting a traditional planetary mapping workflow to the production of training data. We discuss both the pixel scale accuracy of the model, and how qualitative factors can influence the reliability and usability of the output.
We conclude that “landscape level” reliability is critical for the use of the output raster by humans. The output can often be more useful than pixel scale accuracy statistics would suggest, however the product must be treated with caution, and not considered a final arbiter of geological origin. A good understanding of how and why the model classifies different landscape features is vital to interpreting it reliably. When used appropriately the classified raster provides a good indication of the prevalence and distribution of different terrain types, and informs our understanding of the study areas. We thus conclude that it is fit for purpose, and suitable for use in further work
Optimizing the lateral beamforming step for filtered-delay multiply and sum beamforming to improve active contour segmentation using ultrafast ultrasound imaging
As an alternative to delay-and-sum beamforming, a novel beamforming technique called filtered-delay multiply and sum (FDMAS) was introduced recently to improve ultrasound B-mode image quality. Although a considerable amount of work has been performed to evaluate FDMAS performance, no study has yet focused on the beamforming step size, , in the lateral direction. Accordingly, the performance of FDMAS was evaluated in this study by fine-tuning to find its optimal value and improve boundary definition when balloon snake active contour (BSAC) segmentation was applied to a B-mode image in ultrafast imaging. To demonstrate the effect of altering in the lateral direction on FDMAS, measurements were performed on point targets, a tissue-mimicking phantom and in vivo carotid artery, by using the ultrasound array research platform II equipped with one 128-element linear array transducer, which was excited by 2-cycle sinusoidal signals. With 9-angle compounding, results showed that the lateral resolution (LR) of the point target was improved by 67.9% and 81.2%, when measured at −6 dB and −20 dB respectively, when was reduced from to . Meanwhile the image contrast ratio (CR) measured on the CIRS phantom was improved by 10.38 dB at the same reduction and the same number of compounding angles. The enhanced FDMAS results with lower side lobes and less clutter noise in the anechoic regions provides a means to improve boundary definition on a B-mode image when BSAC segmentation is applied
A comparison of LED panels for use in Virtual Production: Findings and recommendations
We evaluate four LED panels from three vendors for use in Virtual Production using experimental conditions. Tests were designed to assess Moire and scan-line artefacts, light reflectivity and grazing angle. We discuss LED manufacturing processes and the implications of these on visual artefacts, power consumption and colour fidelity, and conclude with results and recommendations for LED vendors, practitioners and procurement teams in Virtual Production
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