16,535 research outputs found
Assessing performance of artificial neural networks and re-sampling techniques for healthcare datasets.
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
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EEG microstates: Functional significance and short-term test-retest reliability
Appendix A: Supplementary data to this article can be found online at https://doi. org/10.1016/j.ynirp.2022.100089.Copyright /© 2022 The Authors. EEG signal power, may have clinical relevance; however, their functional significance and test-retest reliability remain unclear. To investigate the functional significance of the canonical EEG microstate classes and their pairwise transitions, and to establish their within-session test-retest reliability, we recorded 36-channel EEGs in 20 healthy volunteers during three eyes-closed conditions: mind-wandering, verbalization (silently repeating the word ‘square’ every 2 s), and visualization (visualizing a square every 2 s). Each condition lasted 3 min and the sequence of three conditions was repeated four times (two runs of two sequence repetitions). The participants' alertness and their sense of effort during the experiment were rated using visual-analogue scales. The EEG data were 2–20 Hz bandpass-filtered and analysed into the four canonical microstate classes: A, B, C, and D. EEG microstate classes C and D were persistently more dominant than classes A and B in all conditions. Of the first-order microstate parameters, average microstate duration was most reliable. The duration of class D microstate was longer during mind-wandering (106.8 ms) than verbalization (102.2 ms) or visualization (99.8 ms), with a concomitantly higher coverage (36.4% vs. 34.7% and 35.2%), but otherwise there was no clear association of the four microstate classes to particular mental states. The test-retest reliability was higher at the beginning of each run, together with higher average alpha power and subjective ratings of alertness. Only the transitions between classes C and D (from C to D in particular) were significantly higher than what would be expected from the respective microstates' occurrences. The transition probabilities, however, did not distinguish between conditions, and their test-retest reliability was overall lower than that of the first-order parameters such as duration and coverage. Further studies are needed to establish the functional significance of the canonical EEG microstate classes. This might be more fruitfully achieved by looking at their complex syntax beyond pairwise transitions. To ensure greater test-retest reliability of microstate parameters, study designs should allow for shorter experimental runs with regular breaks, particularly when using EEG microstates as clinical biomarkers.BIAL Foundation (grant number: 183/16)
Modelling uncertainties for measurements of the H → γγ Channel with the ATLAS Detector at the LHC
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
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European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) Expert Consensus Statement on the state of genetic testing for cardiac diseases.
Unraveling the effect of sex on human genetic architecture
Sex is arguably the most important differentiating characteristic in most mammalian
species, separating populations into different groups, with varying behaviors, morphologies,
and physiologies based on their complement of sex chromosomes, amongst other factors. In
humans, despite males and females sharing nearly identical genomes, there are differences
between the sexes in complex traits and in the risk of a wide array of diseases. Sex provides
the genome with a distinct hormonal milieu, differential gene expression, and environmental
pressures arising from gender societal roles. This thus poses the possibility of observing
gene by sex (GxS) interactions between the sexes that may contribute to some of the
phenotypic differences observed. In recent years, there has been growing evidence of GxS,
with common genetic variation presenting different effects on males and females. These
studies have however been limited in regards to the number of traits studied and/or
statistical power. Understanding sex differences in genetic architecture is of great
importance as this could lead to improved understanding of potential differences in
underlying biological pathways and disease etiology between the sexes and in turn help
inform personalised treatments and precision medicine.
In this thesis we provide insights into both the scope and mechanism of GxS across the
genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK
Biobank. We found small yet widespread differences in genetic architecture across traits
through the calculation of sex-specific heritability, genetic correlations, and sex-stratified
genome-wide association studies (GWAS). We further investigated whether sex-agnostic
(non-stratified) efforts could potentially be missing information of interest, including sex-specific trait-relevant loci and increased phenotype prediction accuracies. Finally, we
studied the potential functional role of sex differences in genetic architecture through sex
biased expression quantitative trait loci (eQTL) and gene-level analyses.
Overall, this study marks a broad examination of the genetics of sex differences. Our findings
parallel previous reports, suggesting the presence of sexual genetic heterogeneity across
complex traits of generally modest magnitude. Furthermore, our results suggest the need to
consider sex-stratified analyses in future studies in order to shed light into possible sex-specific molecular mechanisms
How to Be a God
When it comes to questions concerning the nature of Reality, Philosophers and Theologians have the answers.
Philosophers have the answers that can’t be proven right. Theologians have the answers that can’t be proven wrong.
Today’s designers of Massively-Multiplayer Online Role-Playing Games create realities for a living. They can’t spend centuries mulling over the issues: they have to face them head-on. Their practical experiences can indicate which theoretical proposals actually work in practice.
That’s today’s designers. Tomorrow’s will have a whole new set of questions to answer.
The designers of virtual worlds are the literal gods of those realities. Suppose Artificial Intelligence comes through and allows us to create non-player characters as smart as us. What are our responsibilities as gods? How should we, as gods, conduct ourselves?
How should we be gods
Response of saline reservoir to different phaseCOâ‚‚-brine: experimental tests and image-based modelling
Geological CO₂ storage in saline rocks is a promising method for meeting the target of net zero emission and minimizing the anthropogenic CO₂ emitted into the earth’s atmosphere. Storage of CO₂ in saline rocks triggers CO₂-brine-rock interaction that alters the properties of the rock. Properties of rocks are very crucial for the integrity and efficiency of the storage process. Changes in properties of the reservoir rocks due to CO₂-brine-rock interaction must be well predicted, as some changes can reduce the storage integrity of the reservoir. Considering the thermodynamics, phase behavior, solubility of CO₂ in brine, and the variable pressure-temperature conditions of the reservoir, there will be undissolved CO₂ in a CO₂ storage reservoir alongside the brine for a long time, and there is a potential for phase evolution of the undissolved CO₂. The phase of CO₂ influence the CO₂-brine-rock interaction, different phaseCO₂-brine have a unique effect on the properties of the reservoir rocks, Therefore, this study evaluates the effect of four different phaseCO₂-brine reservoir states on the properties of reservoir rocks using experimental and image-based approach.
Samples were saturated with the different phaseCOâ‚‚-brine, then subjected to reservoir conditions in a triaxial compression test. The representative element volume (REV)/representative element area (REA) for the rock samples was determined from processed digital images, and rock properties were evaluated using digital rock physics and rock image analysis techniques. This research has evaluated the effect of different phaseCOâ‚‚-brine on deformation rate and deformation behavior, bulk modulus, compressibility, strength, and stiffness as well as porosity and permeability of sample reservoir rocks. Changes in pore geometry properties, porosity, and permeability of the rocks in COâ‚‚ storage conditions with different phaseCOâ‚‚-brine have been evaluated using digital rock physics techniques. Microscopic rock image analysis has been applied to provide evidence of changes in micro-fabric, the topology of minerals, and elemental composition of minerals in saline rocks resulting from different phaseCOâ‚‚-br that can exist in a saline COâ‚‚ storage reservoir. It was seen that the properties of the reservoir that are most affected by the scCOâ‚‚-br state of the reservoir include secondary fatigue rate, bulk modulus, shear strength, change in the topology of minerals after saturation as well as change in shape and flatness of pore surfaces. The properties of the reservoir that is most affected by the gCOâ‚‚-br state of the reservoir include primary fatigue rate, change in permeability due to stress, change in porosity due to stress, and change topology of minerals due to stress. For all samples, the roundness and smoothness of grains as well as smoothness of pores increased after compression while the roundness of pores decreased. Change in elemental composition in rock minerals in COâ‚‚-brine-rock interaction was seen to depend on the reactivity of the mineral with COâ‚‚ and/or brine and the presence of brine accelerates such change. Carbon, oxygen, and silicon can be used as index minerals for elemental changes in a COâ‚‚-brine-rock system. The result of this work can be applied to predicting the effect the different possible phases of COâ‚‚ will have on the deformation, geomechanics indices, and storage integrity of giant COâ‚‚ storage fields such as Sleipner, In Salah, etc
Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting
Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others. Existing models use complex statistical models that are often too costly, both computationally and budgetary, or are not applied to downstream applications. Therefore, approaches that use Machine Learning algorithms in conjunction with time-series data are being explored as an alternative to overcome these drawbacks. To this end, this study presents a comparative analysis using simplified rainfall estimation models based on conventional Machine Learning algorithms and Deep Learning architectures that are efficient for these downstream applications. Models based on LSTM, Stacked-LSTM, Bidirectional-LSTM Networks, XGBoost, and an ensemble of Gradient Boosting Regressor, Linear Support Vector Regression, and an Extra-trees Regressor were compared in the task of forecasting hourly rainfall volumes using time-series data. Climate data from 2000 to 2020 from five major cities in the United Kingdom were used. The evaluation metrics of Loss, Root Mean Squared Error, Mean Absolute Error, and Root Mean Squared Logarithmic Error were used to evaluate the models' performance. Results show that a Bidirectional-LSTM Network can be used as a rainfall forecast model with comparable performance to Stacked-LSTM Networks. Among all the models tested, the Stacked-LSTM Network with two hidden layers and the Bidirectional-LSTM Network performed best. This suggests that models based on LSTM-Networks with fewer hidden layers perform better for this approach; denoting its ability to be applied as an approach for budget-wise rainfall forecast applications
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Privacy-aware Smart Home Interface Framework
Smart home user interfaces are pervasive and shared by multiple users who occupy the space. Therefore, they pose a risk to interpersonal privacy of occupants because an individual’s sensitive information can be leaked to other co-occupants (information privacy), or they can be disturbed by intrusions into their personal space (physical privacy) when the co-occupant interacts with the smart home user interfaces. This thesis hypothesises that interpersonal privacy violations can be mitigated by adapting the user interface layer and presents insights into how to achieve usable user interface adaptation to mitigate or minimise interpersonal privacy violations in smart homes.
The thesis reports two case studies and two user studies. The first case study identifies the key characteristics needed to model the rich context of interpersonal privacy violations scenarios. Then it presents knowledge representation models that are required to represent the identified characteristics and evaluates them for adequacy in modelling the context information of interpersonal privacy violation scenarios. The second case study presents a software architecture and a set of algorithms that can detect interpersonal privacy violations and generate usable user interface adaptations. Then it evaluates the architecture and the algorithms for adequacy in generating usable privacy-aware user interface adaptations. The first user study (N=15) evaluates the usability of the adaptive user interfaces generated from the framework where storyboards were used as the stimulant. Extending the findings from the usability study and expanding the coverage of example scenarios, the second user study (N=23) evaluates the overall user experience of the adaptive user interfaces, using video prototypes as the stimulant.
The research demonstrates that the characteristics identified, and the respective knowledge representation models adequately captured the context of interpersonal privacy violation scenarios. Furthermore, the software architecture and the algorithms could detect possible interpersonal privacy violations and generate usable user interface adaptations to mitigate them. The two user studies demonstrate that the adaptive user interfaces, when used in appropriate situations, were a suitable solution for addressing interpersonal privacy violations while providing high usability and a positive user experience. The thesis concludes by providing recommendations for developing privacy-aware user interface adaptations and suggesting future work that can extend this research
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