360 research outputs found

    Senior Recital: Katherine Gould, soprano

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    Autonomous analysis to identify bijels from two-dimensional images

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    Bicontinuous interfacially jammed emulsion gels (bijels) are novel composite materials that can be challenging to manufacture. As a step towards automating production, we have developed a machine learning tool to classify fabrication attempts. We use training and testing data in the form of confocal images from both successful and unsuccessful attempts at bijel fabrication. We then apply machine learning techniques to this data in order to classify whether an image is a bijel or a non-bijel. Our principal approach is to process the images to find their autocorrelation function and structure factor, and from these functions we identify variables that can be used for training a supervised machine learning model to identify a bijel image. We are able to categorise images with reasonable accuracies of 85.4% and 87.5% for two different approaches. We find that using both the liquid and particle channels helps to achieve optimal performance and that successful classification relies on the bijel samples sharing a characteristic length scale. Our second approach is to classify the shapes of the liquid domains directly; the shape descriptors are then used to classify fabrication attempts via a decision tree. We have used an adaptive design approach to find an image pre-processing step that yields the optimal classification results. Again, we find that the characteristic length scale of the images is crucial in performing the classification

    Redefining Leadership in the 21st Century: the view from cybernetics

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    A white paper developed by the ANU School of Cybernetics powered by The Menzies Foundation

    Computerized exposure therapy for Spider Phobia: Effects of cardiac timing and interoceptive ability on subjective and behavioral outcomes

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    Objective: Spider phobia is a common form of anxiety disorder for which exposure therapy is an effective first-line treatment. Motivated by the observed modulation of threat processing by afferent cardiac signals; we tested the hypothesis that interoceptive information concerning cardiovascular arousal can influence the outcomes of computerised exposure therapy for spider phobia. Method: Fifty-three normal healthy participants with high spider phobia scores underwent one of three modified computerised exposure protocols, defined by the timing of exposure to brief spider stimuli within the cardiac cycle: Systole (during afferent baroreceptor firing); Diastole (during baroreceptor-quiescent interbeat interval); Random (non-contingent on cardiac cycle). Outcomes were judged on phobic and anxiety measures and physiological data (skin conductance). Subjects were also rated on interoceptive accuracy. Results: Mancova analysis showed that timing group affected the outcome measures (F(10,80)=2.405, p=0.015) and there was a group interaction with interoception ability (F(15,110)=1.808, p=0.045). Subjective symptom reduction (SPQ) was greatest in the Systolic group relative to the other two groups (Diastolic (t=3.115, ptukey=0.009); Random (t=2.438, ptukey=0.048), with greatest reductions in those participants with lower interoceptive accuracy. Behavioural aversion (BAT) reduced more in cardiac-contingent groups than the non-contingent (Random) group (Diastolic (t=3.295, ptukey=0.005); Systolic (t=2.602, ptukey=0.032). Physiological (SCR) responses remained strongest for spider stimuli presented at cardiac systole. Conclusion: Interoceptive information influences exposure benefit. The reduction in the subjective expression of fear/phobia is facilitated by ‘bottom-up’ afferent signals; while improvement in the behavioural expression is further dependent on ‘top-down’ representation of Interoceptive effects in spider phobia treatment self-related physiology (heart rhythm). Individual interoceptive differences moderate these effects, suggesting means to personalise therapy
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