19,967 research outputs found

    Assessing performance of artificial neural networks and re-sampling techniques for healthcare datasets.

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    Re-sampling methods to solve class imbalance problems have shown to improve classification accuracy by mitigating the bias introduced by differences in class size. However, it is possible that a model which uses a specific re-sampling technique prior to Artificial neural networks (ANN) training may not be suitable for aid in classifying varied datasets from the healthcare industry. Five healthcare-related datasets were used across three re-sampling conditions: under-sampling, over-sampling and combi-sampling. Within each condition, different algorithmic approaches were applied to the dataset and the results were statistically analysed for a significant difference in ANN performance. The combi-sampling condition showed that four out of the five datasets did not show significant consistency for the optimal re-sampling technique between the f1-score and Area Under the Receiver Operating Characteristic Curve performance evaluation methods. Contrarily, the over-sampling and under-sampling condition showed all five datasets put forward the same optimal algorithmic approach across performance evaluation methods. Furthermore, the optimal combi-sampling technique (under-, over-sampling and convergence point), were found to be consistent across evaluation measures in only two of the five datasets. This study exemplifies how discrete ANN performances on datasets from the same industry can occur in two ways: how the same re-sampling technique can generate varying ANN performance on different datasets, and how different re-sampling techniques can generate varying ANN performance on the same dataset

    Modelling uncertainties for measurements of the H → γγ Channel with the ATLAS Detector at the LHC

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    The Higgs boson to diphoton (H → γγ) branching ratio is only 0.227 %, but this final state has yielded some of the most precise measurements of the particle. As measurements of the Higgs boson become increasingly precise, greater import is placed on the factors that constitute the uncertainty. Reducing the effects of these uncertainties requires an understanding of their causes. The research presented in this thesis aims to illuminate how uncertainties on simulation modelling are determined and proffers novel techniques in deriving them. The upgrade of the FastCaloSim tool is described, used for simulating events in the ATLAS calorimeter at a rate far exceeding the nominal detector simulation, Geant4. The integration of a method that allows the toolbox to emulate the accordion geometry of the liquid argon calorimeters is detailed. This tool allows for the production of larger samples while using significantly fewer computing resources. A measurement of the total Higgs boson production cross-section multiplied by the diphoton branching ratio (σ × Bγγ) is presented, where this value was determined to be (σ × Bγγ)obs = 127 ± 7 (stat.) ± 7 (syst.) fb, within agreement with the Standard Model prediction. The signal and background shape modelling is described, and the contribution of the background modelling uncertainty to the total uncertainty ranges from 18–2.4 %, depending on the Higgs boson production mechanism. A method for estimating the number of events in a Monte Carlo background sample required to model the shape is detailed. It was found that the size of the nominal γγ background events sample required a multiplicative increase by a factor of 3.60 to adequately model the background with a confidence level of 68 %, or a factor of 7.20 for a confidence level of 95 %. Based on this estimate, 0.5 billion additional simulated events were produced, substantially reducing the background modelling uncertainty. A technique is detailed for emulating the effects of Monte Carlo event generator differences using multivariate reweighting. The technique is used to estimate the event generator uncertainty on the signal modelling of tHqb events, improving the reliability of estimating the tHqb production cross-section. Then this multivariate reweighting technique is used to estimate the generator modelling uncertainties on background V γγ samples for the first time. The estimated uncertainties were found to be covered by the currently assumed background modelling uncertainty

    Développement d’un système intelligent de reconnaissance automatisée pour la caractérisation des états de surface de la chaussée en temps réel par une approche multicapteurs

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    Le rôle d’un service dédié à l’analyse de la météo routière est d’émettre des prévisions et des avertissements aux usagers quant à l’état de la chaussée, permettant ainsi d’anticiper les conditions de circulations dangereuses, notamment en période hivernale. Il est donc important de définir l’état de chaussée en tout temps. L’objectif de ce projet est donc de développer un système de détection multicapteurs automatisée pour la caractérisation en temps réel des états de surface de la chaussée (neige, glace, humide, sec). Ce mémoire se focalise donc sur le développement d’une méthode de fusion de données images et sons par apprentissage profond basée sur la théorie de Dempster-Shafer. Les mesures directes pour l’acquisition des données qui ont servi à l’entrainement du modèle de fusion ont été effectuées à l’aide de deux capteurs à faible coût disponibles dans le commerce. Le premier capteur est une caméra pour enregistrer des vidéos de la surface de la route. Le second capteur est un microphone pour enregistrer le bruit de l’interaction pneu-chaussée qui caractérise chaque état de surface. La finalité de ce système est de pouvoir fonctionner sur un nano-ordinateur pour l’acquisition, le traitement et la diffusion de l’information en temps réel afin d’avertir les services d’entretien routier ainsi que les usagers de la route. De façon précise, le système se présente comme suit :1) une architecture d’apprentissage profond classifiant chaque état de surface à partir des images issues de la vidéo sous forme de probabilités ; 2) une architecture d’apprentissage profond classifiant chaque état de surface à partir du son sous forme de probabilités ; 3) les probabilités issues de chaque architecture ont été ensuite introduites dans le modèle de fusion pour obtenir la décision finale. Afin que le système soit léger et moins coûteux, il a été développé à partir d’architectures alliant légèreté et précision à savoir Squeeznet pour les images et M5 pour le son. Lors de la validation, le système a démontré une bonne performance pour la détection des états surface avec notamment 87,9 % pour la glace noire et 97 % pour la neige fondante

    Image classification over unknown and anomalous domains

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    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

    Data-to-text generation with neural planning

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    In this thesis, we consider the task of data-to-text generation, which takes non-linguistic structures as input and produces textual output. The inputs can take the form of database tables, spreadsheets, charts, and so on. The main application of data-to-text generation is to present information in a textual format which makes it accessible to a layperson who may otherwise find it problematic to understand numerical figures. The task can also automate routine document generation jobs, thus improving human efficiency. We focus on generating long-form text, i.e., documents with multiple paragraphs. Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or its variants. These models generate fluent (but often imprecise) text and perform quite poorly at selecting appropriate content and ordering it coherently. This thesis focuses on overcoming these issues by integrating content planning with neural models. We hypothesize data-to-text generation will benefit from explicit planning, which manifests itself in (a) micro planning, (b) latent entity planning, and (c) macro planning. Throughout this thesis, we assume the input to our generator are tables (with records) in the sports domain. And the output are summaries describing what happened in the game (e.g., who won/lost, ..., scored, etc.). We first describe our work on integrating fine-grained or micro plans with data-to-text generation. As part of this, we generate a micro plan highlighting which records should be mentioned and in which order, and then generate the document while taking the micro plan into account. We then show how data-to-text generation can benefit from higher level latent entity planning. Here, we make use of entity-specific representations which are dynam ically updated. The text is generated conditioned on entity representations and the records corresponding to the entities by using hierarchical attention at each time step. We then combine planning with the high level organization of entities, events, and their interactions. Such coarse-grained macro plans are learnt from data and given as input to the generator. Finally, we present work on making macro plans latent while incrementally generating a document paragraph by paragraph. We infer latent plans sequentially with a structured variational model while interleaving the steps of planning and generation. Text is generated by conditioning on previous variational decisions and previously generated text. Overall our results show that planning makes data-to-text generation more interpretable, improves the factuality and coherence of the generated documents and re duces redundancy in the output document

    RNA pull-down-confocal nanoscanning (RP-CONA), a novel method for studying RNA/protein interactions in cell extracts that detected potential drugs for Parkinson’s disease targeting RNA/HuR complexes

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    MicroRNAs (miRNAs, miRs) are a class of small non-coding RNAs that regulate gene expression through specific base-pair targeting. The functional mature miRNAs usually undergo a two-step cleavage from primary miRNAs (pri-miRs), then precursor miRNAs (pre-miRs). The biogenesis of miRNAs is tightly controlled by different RNA-binding proteins (RBPs). The dysregulation of miRNAs is closely related to a plethora of diseases. Targeting miRNA biogenesis is becoming a promising therapeutic strategy. HuR and MSI2 are both RBPs. MiR-7 is post-transcriptionally inhibited by the HuR/MSI2 complex, through a direct interaction between HuR and the conserved terminal loop (CTL) of pri-miR-7-1. Small molecules dissociating pri-miR-7/HuR interaction may induce miR-7 production. Importantly, the miR-7 levels are negatively correlated with Parkinson’s disease (PD). PD is a common, incurable neurodegenerative disease causing serious motor deficits. A hallmark of PD is the presence of Lewy bodies in the human brain, which are inclusion bodies mainly composed of an aberrantly aggregated protein named α-synuclein (α-syn). Decreasing α-syn levels or preventing α-syn aggregation are under investigation as PD treatments. Notably, α-syn is negatively regulated by several miRNAs, including miR-7, miR-153, miR-133b and others. One hypothesis is that elevating these miRNA levels can inhibit α-syn expression and ameliorate PD pathologies. In this project, we identified miR-7 as the most effective α-syn inhibitor, among the miRNAs that are downregulated in PD, and with α-syn targeting potentials. We also observed potential post-transcriptional inhibition on miR-153 biogenesis in neuroblastoma, which may help to uncover novel therapeutic targets towards PD. To identify miR-7 inducers that benefit PD treatment by repressing α-syn expression, we developed a novel technique RNA Pull-down Confocal Nanoscaning (RP-CONA) to monitor the binding events between pri-miR-7 and HuR. By attaching FITC-pri-miR-7-1-CTL-biotin to streptavidin-coated agarose beads and incubating them in human cultured cell lysates containing overexpressed mCherry-HuR, the bound RNA and protein can be visualised as quantifiable fluorescent rings in corresponding channels in a confocal high-content image system. A pri-miR-7/HuR inhibitor can decrease the relative mCherry/FITC intensity ratio in RP-CONA. With this technique, we performed several small-scale screenings and identified that a bioflavonoid, quercetin can largely dissociate the pri-miR-7/HuR interaction. Further studies proved that quercetin was an effective miR-7 inducer as well as α-syn inhibitor in HeLa cells. To understand the mechanism of quercetin mediated α-syn inhibition, we tested the effects of quercetin treatment with miR-7-1 and HuR knockout HeLa cells. We found that HuR was essential in this pathway, while miR-7 hardly contributed to the α-syn inhibition. HuR can directly bind an AU-rich element (ARE) at the 3’ untranslated region (3’-UTR) of α-syn mRNA and promote translation. We believe quercetin mainly disrupts the ARE/HuR interaction and disables the HuR-induced α-syn expression. In conclusion, we developed and optimised RP-CONA, an on-bead, lysate-based technique detecting RNA/protein interactions, as well as identifying RNA/protein modulators. With RP-CONA, we found quercetin inducing miR-7 biogenesis, and inhibiting α-syn expression. With these beneficial effects, quercetin has great potential to be applied in the clinic of PD treatment. Finally, RP-CONA can be used in many other RNA/protein interactions studies

    Detection of Hyperpartisan news articles using natural language processing techniques

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    Yellow journalism has increased the spread of hyperpartisan news on the internet. It is very difficult for online news article readers to distinguish hyperpartisan news articles from mainstream news articles. There is a need for an automated model that can detect hyperpartisan news on the internet and tag them as hyperpartisan so that it is very easy for readers to avoid that news. A hyperpartisan news detection article was developed by using three different natural language processing techniques named BERT, ELMo, and Word2vec. This research used the bi-article dataset published at SEMEVAL-2019. The ELMo word embeddings which are trained on a Random forest classifier has got an accuracy of 0.88, which is much better than other state of art models. The BERT and Word2vec models have got the same accuracy of 0.83. This research tried different sentence input lengths to BERT and proved that BERT can extract context from local words. Evidenced from the described ML models, this study will assist the governments, news’ readers, and other political stakeholders to detect any hyperpartisan news, and also helps policy to track, and regulate, misinformation about the political parties and their leaders

    Increased lifetime of Organic Photovoltaics (OPVs) and the impact of degradation, efficiency and costs in the LCOE of Emerging PVs

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    Emerging photovoltaic (PV) technologies such as organic photovoltaics (OPVs) and perovskites (PVKs) have the potential to disrupt the PV market due to their ease of fabrication (compatible with cheap roll-to-roll processing) and installation, as well as their significant efficiency improvements in recent years. However, rapid degradation is still an issue present in many emerging PVs, which must be addressed to enable their commercialisation. This thesis shows an OPV lifetime enhancing technique by adding the insulating polymer PMMA to the active layer, and a novel model for quantifying the impact of degradation (alongside efficiency and cost) upon levelized cost of energy (LCOE) in real world emerging PV installations. The effect of PMMA morphology on the success of a ternary strategy was investigated, leading to device design guidelines. It was found that either increasing the weight percent (wt%) or molecular weight (MW) of PMMA resulted in an increase in the volume of PMMA-rich islands, which provided the OPV protection against water and oxygen ingress. It was also found that adding PMMA can be effective in enhancing the lifetime of different active material combinations, although not to the same extent, and that processing additives can have a negative impact in the devices lifetime. A novel model was developed taking into account realistic degradation profile sourced from a literature review of state-of-the-art OPV and PVK devices. It was found that optimal strategies to improve LCOE depend on the present characteristics of a device, and that panels with a good balance of efficiency and degradation were better than panels with higher efficiency but higher degradation as well. Further, it was found that low-cost locations were more favoured from reductions in the degradation rate and module cost, whilst high-cost locations were more benefited from improvements in initial efficiency, lower discount rates and reductions in install costs

    Epilepsy Mortality: Leading Causes of Death, Co-morbidities, Cardiovascular Risk and Prevention

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    a reuptake inhibitor selectively prevents seizure-induced sudden death in the DBA/1 mouse model of sudden unexpected ... Bilateral lesions of the fastigial nucleus prevent the recovery of blood pressure following hypotension induced by ..
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