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In recent years, there has been a significant increase in the usage of audience engagement platforms, which have allowed for engaging interactions between presenters and their audiences. The increased popularity of the platforms comes from the fact that engaging and interactive presentations have been shown to improve learning outcomes and create positive presentation experiences. However, using the platforms does not guarantee that your audience is engaged and participating. Given that the added value of engaging presentations only applies if the audience is actually engaged, it increases the need to know if and how engaged your audience is. The usage of audience engagement platforms has allowed for new ways of engagement to be studied. By utilizing the data gathered from the interactive presentation sessions, engagement can be studied and quantified through the modeling of the data. As the usage of audience engagement platforms and the study of presentation engagement is relatively new, there exists a limited amount of labeled data quantifying the level of engagement during presentations. To model the data, machine learning models should therefore be trained to generalize by being exposed to a limited number of presentation samples. This technique of training machine learning models is also referred to as few-shot learning. Distance-based machine learning models are defined in this study as models that make classifications and inferences by calculating distances between observations or observation class representations. Distance-based models have previously shown relatively good performance in few-shot learning applications, and interest therefore lies in expanding their application areas. This study presents a comparative evaluation of distance- and non-distance-based machine learning models given the problem of classifying presentations as being engaged or non-engaged, and estimating presentation class likelihoods in a few-shot learning context. A presentation-l, Under de senaste åren har det skett en betydande ökning av användningen av plattformar för publikengagemang, vilket har möjliggjort engagerande interaktioner mellan presentatörer och deras publik. Plattformarnas ökade popularitet kommer från det faktum att engagerande och interaktiva presentationer har visat sig förbättra läranderesultat och skapa positiva presentationsupplevelser. Att använda plattformarna garanterar dock inte att din publik är engagerad och deltagande. Med tanke på att mervärdet av engagerande presentationer bara gäller om publiken faktiskt är engagerad, ökar det behovet av att veta om och hur engagerad din publik är. Användningen av plattformar för publikengagemang har gjort det möjligt att på nya sätt studera engagemang. Genom att använda data som samlats in från de interaktiva presentationssessionerna kan engagemang studeras och kvantifieras genom modellering av data. Eftersom användandet av plattformar för publikengagemang och studien av presentationsengagemang är relativt nytt, finns det en begränsad mängd märkt data som kvantifierar nivån av engagemang under presentationerna. För att modellera datan så bör maskininlärningsmodeller tränas att generalisera genom att utsättas för ett begränsad antal presentations observationer. Denna teknik för att träna inlärningsmodeller kallas också few-shot lärande. Distans-baserade maskininlärningsmodeller definieras i denna studie som modeller som gör klassificeringar genom att beräkna avstånd mellan observationer eller observationsklass representationer. Distans-baserade modeller har tidigare visat relativt goda resultat i few-shot inlärning problem, och intresset ligger därför i att utöka deras tillämpningsområden. Denna studie presenterar en jämförande utvärdering av distans- och icke-distans baserade maskininlärningsmodeller givet problemet med att klassificera presentationer som engagerade eller icke-engagerade, och uppskattning av presentation klasssannolikheter i ett few-shot inlärnings sammanhang.

In recent years, there has been a significant increase in the usage of audience engagement platforms, which have allowed for engaging interactions between presenters and their audiences. The increased popularity of the platforms comes from the fact that engaging and interactive presentations have been shown to improve learning outcomes and create positive presentation experiences. However, using the platforms does not guarantee that your audience is engaged and participating. Given that the added value of engaging presentations only applies if the audience is actually engaged, it increases the need to know if and how engaged your audience is. The usage of audience engagement platforms has allowed for new ways of engagement to be studied. By utilizing the data gathered from the interactive presentation sessions, engagement can be studied and quantified through the modeling of the data. As the usage of audience engagement platforms and the study of presentation engagement is relatively new, there exists a limited amount of labeled data quantifying the level of engagement during presentations. To model the data, machine learning models should therefore be trained to generalize by being exposed to a limited number of presentation samples. This technique of training machine learning models is also referred to as few-shot learning. Distance-based machine learning models are defined in this study as models that make classifications and inferences by calculating distances between observations or observation class representations. Distance-based models have previously shown relatively good performance in few-shot learning applications, and interest therefore lies in expanding their application areas. This study presents a comparative evaluation of distance- and non-distance-based machine learning models given the problem of classifying presentations as being engaged or non-engaged, and estimating presentation class likelihoods in a few-shot learning context. A presentation-l, Under de senaste åren har det skett en betydande ökning av användningen av plattformar för publikengagemang, vilket har möjliggjort engagerande interaktioner mellan presentatörer och deras publik. Plattformarnas ökade popularitet kommer från det faktum att engagerande och interaktiva presentationer har visat sig förbättra läranderesultat och skapa positiva presentationsupplevelser. Att använda plattformarna garanterar dock inte att din publik är engagerad och deltagande. Med tanke på att mervärdet av engagerande presentationer bara gäller om publiken faktiskt är engagerad, ökar det behovet av att veta om och hur engagerad din publik är. Användningen av plattformar för publikengagemang har gjort det möjligt att på nya sätt studera engagemang. Genom att använda data som samlats in från de interaktiva presentationssessionerna kan engagemang studeras och kvantifieras genom modellering av data. Eftersom användandet av plattformar för publikengagemang och studien av presentationsengagemang är relativt nytt, finns det en begränsad mängd märkt data som kvantifierar nivån av engagemang under presentationerna. För att modellera datan så bör maskininlärningsmodeller tränas att generalisera genom att utsättas för ett begränsad antal presentations observationer. Denna teknik för att träna inlärningsmodeller kallas också few-shot lärande. Distans-baserade maskininlärningsmodeller definieras i denna studie som modeller som gör klassificeringar genom att beräkna avstånd mellan observationer eller observationsklass representationer. Distans-baserade modeller har tidigare visat relativt goda resultat i few-shot inlärning problem, och intresset ligger därför i att utöka deras tillämpningsområden. Denna studie presenterar en jämförande utvärdering av distans- och icke-distans baserade maskininlärningsmodeller givet problemet med att klassificera presentationer som engagerade eller icke-engagerade, och uppskattning av presentation klasssannolikheter i ett few-shot inlärnings sammanhang.

My dissertation is composed of three chapters. In the first, I study the incentive role of information – how the strategic release of information can induce an agent to exert more effort on a project. More specifically, I focus on how feedback can be provided to a worker who is uninformed about the progress they make on a long term project. I show that delaying feedback about their performance can induce the worker to continue working on the project longer than they would were they to learn about their performance without delay. Negative feedback, due to the absence of good news, received in the early stages of the project can cause them to quit prematurely. In the second chapter, I study a model of matching between individuals and institutions. Matching models allow researchers to identify optimal allocations of individuals to school seats, medical residency programs and other positions over which individuals have preferences and for which they may differ in suitability. While we know that in models in which individuals only care about the institution they match with, stable matchings always exist, I show that when individuals also care about the the number of matches made by the institution they join, stable matchings no longer exist in general. I show that stable matchings can only be found under a set of conditions I identify. Relaxing any of these conditions leads to examples of markets with no stable matchings. In the third chapter, I set out to understand why elected politicians choose to toe the party line instead of voting on issues according to their own preferences. I find that despite the short term benefits of voting for their preferred policies, there are long-term benefits from coordinating their voting behavior among like-minded legislators. These findings provide a rationale for why political parties form among politicians with similar policy positions.

My dissertation is composed of three chapters. In the first, I study the incentive role of information – how the strategic release of information can induce an agent to exert more effort on a project. More specifically, I focus on how feedback can be provided to a worker who is uninformed about the progress they make on a long term project. I show that delaying feedback about their performance can induce the worker to continue working on the project longer than they would were they to learn about their performance without delay. Negative feedback, due to the absence of good news, received in the early stages of the project can cause them to quit prematurely. In the second chapter, I study a model of matching between individuals and institutions. Matching models allow researchers to identify optimal allocations of individuals to school seats, medical residency programs and other positions over which individuals have preferences and for which they may differ in suitability. While we know that in models in which individuals only care about the institution they match with, stable matchings always exist, I show that when individuals also care about the the number of matches made by the institution they join, stable matchings no longer exist in general. I show that stable matchings can only be found under a set of conditions I identify. Relaxing any of these conditions leads to examples of markets with no stable matchings. In the third chapter, I set out to understand why elected politicians choose to toe the party line instead of voting on issues according to their own preferences. I find that despite the short term benefits of voting for their preferred policies, there are long-term benefits from coordinating their voting behavior among like-minded legislators. These findings provide a rationale for why political parties form among politicians with similar policy positions.

Knowledge of family planning has increased to 87% among currently married women. However, it is uses still lagging as, about 22% of young married women and 26% of sexually active unmarried women in this country are reported having an unmet need for family planning. Ethiopia is one of the highest unmet need for family planning according to EDHS-2016 but the level and factors that determine unmet need for FP are not well understood. This study assess the magnitude and factors associated with unmet need for family planning among married women of urban and rural communities in Jigjiga city administration. A community based cross-sectional study design was used, with 375 households. Sampling frame was used listing all households in the selected kebeles (smallest administration units) in which married women resided. A structured questionnaire adopted from literature was used. Data was checked, edited, coded and entered into EPIDATA version 3.1 and transported to SPSS version 23 for analysis. Bivariate and multivariate logistic regression was carried out to see any associations between outcome and explanatory variables and P-Value of 0.05 was considered significant. This study revealed that 22.6 % of married women had unmet need for family planning out of which, 68(21.5%) was for spacing and 4(1.1%) for limiting.In this study, unavailability of health facility that provide family planning services was 2.3 (AOR=2.31, 95%CI: 1.087, 4.912 1.042, 4.3) times more likely to have unmet need for FP than those who had. Women who desire to have more than or equal to ten children were 3.7 (AOR=3.7, 95%CI:1.427, 9.822) times more likely to have unmet need for FP than those who desired to have less than ten children and FP practice (non-user and ever-users) were 8 [(AOR=8.8, 95%CI: 1.327, 58.61) and (AOR=8.5, 95%CI: 1.552, 46.04)] times more likely to have unmet need for Family planning than those of current users. It also concluded that the overall prevalence of unmet need for FP amon

Knowledge of family planning has increased to 87% among currently married women. However, it is uses still lagging as, about 22% of young married women and 26% of sexually active unmarried women in this country are reported having an unmet need for family planning. Ethiopia is one of the highest unmet need for family planning according to EDHS-2016 but the level and factors that determine unmet need for FP are not well understood. This study assess the magnitude and factors associated with unmet need for family planning among married women of urban and rural communities in Jigjiga city administration. A community based cross-sectional study design was used, with 375 households. Sampling frame was used listing all households in the selected kebeles (smallest administration units) in which married women resided. A structured questionnaire adopted from literature was used. Data was checked, edited, coded and entered into EPIDATA version 3.1 and transported to SPSS version 23 for analysis. Bivariate and multivariate logistic regression was carried out to see any associations between outcome and explanatory variables and P-Value of 0.05 was considered significant. This study revealed that 22.6 % of married women had unmet need for family planning out of which, 68(21.5%) was for spacing and 4(1.1%) for limiting.In this study, unavailability of health facility that provide family planning services was 2.3 (AOR=2.31, 95%CI: 1.087, 4.912 1.042, 4.3) times more likely to have unmet need for FP than those who had. Women who desire to have more than or equal to ten children were 3.7 (AOR=3.7, 95%CI:1.427, 9.822) times more likely to have unmet need for FP than those who desired to have less than ten children and FP practice (non-user and ever-users) were 8 [(AOR=8.8, 95%CI: 1.327, 58.61) and (AOR=8.5, 95%CI: 1.552, 46.04)] times more likely to have unmet need for Family planning than those of current users. It also concluded that the overall prevalence of unmet need for FP amon

Knowledge of family planning has increased to 87% among currently married women. However, it is uses still lagging as, about 22% of young married women and 26% of sexually active unmarried women in this country are reported having an unmet need for family planning. Ethiopia is one of the highest unmet need for family planning according to EDHS-2016 but the level and factors that determine unmet need for FP are not well understood. This study assess the magnitude and factors associated with unmet need for family planning among married women of urban and rural communities in Jigjiga city administration. A community based cross-sectional study design was used, with 375 households. Sampling frame was used listing all households in the selected kebeles (smallest administration units) in which married women resided. A structured questionnaire adopted from literature was used. Data was checked, edited, coded and entered into EPIDATA version 3.1 and transported to SPSS version 23 for analysis. Bivariate and multivariate logistic regression was carried out to see any associations between outcome and explanatory variables and P-Value of 0.05 was considered significant. This study revealed that 22.6 % of married women had unmet need for family planning out of which, 68(21.5%) was for spacing and 4(1.1%) for limiting.In this study, unavailability of health facility that provide family planning services was 2.3 (AOR=2.31, 95%CI: 1.087, 4.912 1.042, 4.3) times more likely to have unmet need for FP than those who had. Women who desire to have more than or equal to ten children were 3.7 (AOR=3.7, 95%CI:1.427, 9.822) times more likely to have unmet need for FP than those who desired to have less than ten children and FP practice (non-user and ever-users) were 8 [(AOR=8.8, 95%CI: 1.327, 58.61) and (AOR=8.5, 95%CI: 1.552, 46.04)] times more likely to have unmet need for Family planning than those of current users. It also concluded that the overall prevalence of unmet need for FP amon

Feature construction (FC) refers to a process that uses the original features to construct new features with better discrimination ability. Particle Swarm Optimisation (PSO) is an effective search technique that has been successfully utilised in FC. However, the application of PSO for feature construction using high dimensional data has been a challenge due to its large search space and high computational cost. Moreover, unnecessary features that were irrelevant, redundant and contained noise were constructed when PSO was applied to the whole feature. Therefore, the main purpose of this paper is to select the most informative features and construct new features from the selected features for a better classification performance. The feature clustering methods were used to aggregate similar features into clusters, whereby the dimensionality of the data was lowered by choosing representative features from every cluster to form the final feature subset. The clustering of each features are proven to be accurate in feature selection (FS), however, only one study investigated its application in FC for classification. The study identified some limitations, such as the implementation of only two binary classes and the decreasing accuracy of the data. This paper proposes a cluster based PSO feature construction approach called ClusPSOFC. The Redundancy-Based Feature Clustering (RFC) algorithm was applied to choose the most informative features from the original data, while PSO was used to construct new features from those selected by RFC. Experimental results were obtained by using six UCI data sets and six high-dimensional data to demonstrate the efficiency of the proposed method when compared to the original full features, other PSO based FC methods, and standard genetic programming based feature construction (GPFC). Hence, the ClusPSOFC method is effective for feature construction in the classification of high dimensional data.

Saeed Azizi, Abdirahman Osman, Omer Ali Faculty of Medicine, St George’s Hospital Medical School, London, UKWe read the article by Vo et al1 with great interest. Vo et al1 highlighted that multimedia can act as an adjunct for consenting patients for cataract surgery. The article further brings to light the importance of the consent process and how this may change in the future with further accessibility to multimedia.View the original paper by Vo and colleagues.

C Anwar A Chahal, Omer Ali, Ross J Hunter, Richard J SchillingDepartment of Cardiology Research, St Bartholomew's Hospital, London, United KingdomAbstract: Atrial fibrillation (AF) is a substantial cause of mortality and morbidity in the Western world. It is a massive burden on health care systems, and its prevalence is expected to double over the next 20 years. Trials evaluating antiarrhythmic drugs or catheter ablation have focused on recurrence of arrhythmia, perhaps neglecting outcomes relevant to patients, such as symptoms, need for antiarrhythmic drugs, need for hospitalization, and rates of stroke and death. An association has been demonstrated between sinus rhythm and survival in several studies, and there is evidence emerging that successful catheter ablation may reduce rates of stroke and death. Similarly, dronedarone has been shown to reduce hospitalizations and death in patients with paroxysmal AF or persistent AF of recent onset, although it may cause adverse events in permanent AF. New antiarrhythmic drugs are a welcome addition to the armamentarium, since there are limitations to current antiarrhythmic drugs. In particular, sotalol, flecainide, and propafenone cannot be used safely in those with structural heart disease, and amiodarone has important adverse reactions that limit long-term use. Indeed, the use of conventional antiarrhythmic drugs may negate any survival benefit derived from maintaining sinus rhythm. Although dronedarone appears promising with respect to hard endpoints such as stroke and death in certain patients, it may not be safe for those with heart failure or those with permanent AF. Furthermore, the trials suggesting that dronedarone may impact on these endpoints were compared with placebo rather than with an active comparator group. Further "head-to-head" comparisons between dronedarone and other antiarrhythmic drugs are needed to determine whether this property is unique to dronedarone alone.Keywords: at

The rate of elapsed polymerization of polyhydroxyethylacrylate in gelatin has been studied to investigate the effect of co-monomers consumption at a given dose. The polymer gel dosimeters consisted of 2%-4% N,N-methyelene-bis-acrylamide cross-linker, 2%-4% 2-hydroxyethylacrylate monomer and gelatin at 3% and 5%. The dosimeters were irradiated by using 60Co teletherapy γ-ray source up to 20 Gy at a constant dose rate. The relaxation rate of water proton in the dosimeters at different doses and co-monomer concentrations were measured using a nuclear magnetic resonance spectroscopy. The rate of elapsed polymerization decreases with increasing the dose and the initial concentration of co-monomers. The rate of consumption of co-monomers increases with an increase of the polymerization and the gelatin content of the polymer gel.

The effects of varying the concentrations of cross-linker N, N-methyelene-bis-acrylamide (BIS) from 2% to 4%, and 2-hydroxyethylacrylate(HEA) monomer from 2% to 4% at 5% gelatin on the dose response of BIS–HEA–gelatin (BHEAG) aqueous polymer gel dosimeters have been studied using magnetic resonance imaging (MRI) for relaxation rate (R2) of water proton. The dosimeters were irradiated with 60Co teletherapy -ray source at a constant dose rate, receiving doses up to 30 Gy. The radiation polymerization occurs and increases with increasing initial dose. R2 is found to decrease mono-exponentially with depth inside the polymer gel and depend strongly upon the initial concentrations of co-monomers (HEA and BIS). Dose–depth map for BHEAG gel was determined for different concentrations of co-monomer (HEA and BIS). The percentage dose depth was also evaluated which leads to a good agreement with the ionization chamber measurements.

The effects of varying the concentrations of cross-linker N, N-methyelene-bis-acrylamide (BIS) from 2% to 4%, and 2-hydroxyethylacrylate(HEA) monomer from 2% to 4% at 5% gelatin on the dose response of BIS–HEA–gelatin (BHEAG) aqueous polymer gel dosimeters have been studied using magnetic resonance imaging (MRI) for relaxation rate (R2) of water proton. The dosimeters were irradiated with 60Co teletherapy -ray source at a constant dose rate, receiving doses up to 30 Gy. The radiation polymerization occurs and increases with increasing initial dose. R2 is found to decrease mono-exponentially with depth inside the polymer gel and depend strongly upon the initial concentrations of co-monomers (HEA and BIS). Dose–depth map for BHEAG gel was determined for different concentrations of co-monomer (HEA and BIS). The percentage dose depth was also evaluated which leads to a good agreement with the ionization chamber measurements.