Bayesian framework for characterizing cryptocurrency market dynamics, structural dependency, and volatility using potential field

Identifying the structural dependence between the cryptocurrencies and predicting market trend are fundamental for effective portfolio management in cryptocurrency trading. In this paper, we present a unified Bayesian framework based on potential field theory and Gaussian Process to characterize the structural dependency of various cryptocurrencies, using historic price information. The following are our significant contributions: (i) Proposed a novel model for cryptocurrency price movements as a trajectory of a dynamical system governed by a time-varying non-linear potential field. (ii) Validated the existence of the non-linear potential function in cryptocurrency market through Lyapunov stability analysis. (iii) Developed a Bayesian framework for inferring the non-linear potential function from observed cryptocurrency prices. (iv) Proposed that attractors and repellers inferred from the potential field are reliable cryptocurrency market indicators, surpassing existing attributes, such as, mean, open price or close price of an observation window, in the literature. (v) Analysis of cryptocurrency market during various Bitcoin crash durations from April 2017 to November 2021, shows that attractors captured the market trend, volatility, and correlation. In addition, attractors aids explainability and visualization. (vi) The structural dependence inferred by the proposed approach was found to be consistent with results obtained using the popular wavelet coherence approach. (vii) The proposed market indicators (attractors and repellers) can be used to improve the prediction performance of state-of-art deep learning price prediction models. As, an example, we show improvement in Litecoin price prediction up to a horizon of 12 days

Risk of Suicide among Women Survived Domestic Violence in Erbil Governorate

Background and objectives: Domestic violence is a global issue leading to many medical and mental health consequences. A stable family relationship is mandatory for physical and mental health. The current study aimed at assessing the risk of suicide as a consequence of domestic violence among the survived women in Erbil governorate. Methods: A cross-sectional study was conducted from 1st January 2018 to 31st December 2018. A sample of 105 women survived from domestic violence was recruited through a non-probability snowball sampling technique. Data were collected through direct interview with survivals using an adapted version of the ready-made questionnaire format of Columbia-Suicide Severity Rating Scale. The questionnaire was used for interviewing the women about (socio-demographic, violence, and risk of suicide). The validity and reliability of the instrument was checked. Data were analyzed by using the frequency, percentage and fisher exact test from the Statistical Package for Social Science version 23. Results: The results of the study revealed that, the mean age of the study sample was 33.16 years old. 62.9% were married, and 63.8% were housewives. 33.3% of violence conducted was marital rape, in 26.2% of the cases; the violence was continuous throughout the past year. 76.2% of women wished for death and 57.1% thought of suicide. The suicidal risk was mostly linked to rape and sexual violence, were 100% of raped cases wished for death, and 62.5% of them had set a suicidal plan. Conclusion: Domestic violence has a direct relation to the risk of suicide among women survived domestic violence

Dynamics of Information Diffusion and Social Sensing

Statistical inference using social sensors is an area that has witnessed remarkable progress and is relevant in applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This chapter presents a tutorial description of four important aspects of sensing-based information diffusion in social networks from a communications/signal processing perspective. First, diffusion models for information exchange in large scale social networks together with social sensing via social media networks such as Twitter is considered. Second, Bayesian social learning models and risk averse social learning is considered with applications in finance and online reputation systems. Third, the principle of revealed preferences arising in micro-economics theory is used to parse datasets to determine if social sensors are utility maximizers and then determine their utility functions. Finally, the interaction of social sensors with YouTube channel owners is studied using time series analysis methods. All four topics are explained in the context of actual experimental datasets from health networks, social media and psychological experiments. Also, algorithms are given that exploit the above models to infer underlying events based on social sensing. The overview, insights, models and algorithms presented in this chapter stem from recent developments in network science, economics and signal processing. At a deeper level, this chapter considers mean field dynamics of networks, risk averse Bayesian social learning filtering and quickest change detection, data incest in decision making over a directed acyclic graph of social sensors, inverse optimization problems for utility function estimation (revealed preferences) and statistical modeling of interacting social sensors in YouTube social networks.Comment: arXiv admin note: text overlap with arXiv:1405.112

Detection, estimation and control in online social media

Due to large scale use of online social media there has been growing interest in modeling and analysis of data from online social media. The unifying theme of this thesis is to develop a set of mathematical tools for detection, estimation and control in online social media. The following are the main contributions of this thesis: Chapter 2 deals with nonparametric change detection for dynamic utility maximization agents. Using the revealed preference framework, necessary and sufficient conditions for detecting the change point are derived. In the presence of noisy measurements, we construct a decision test to check for dynamic utility maximization behaviour and the change point. Experiments on the Yahoo! Tech Buzz dataset show that the framework can be used to detect changes in ground truth using online search data. Chapter 3 studies engagement dynamics and sensitivity analysis of YouTube videos. Using machine learning and sensitivity analysis techniques it is shown that the video view count is sensitive to 5 meta-level features. In addition, changing the meta-level after the video has been posted increases the popularity of the video. In addition, we examine how the social dynamics of a YouTube channel affect it's popularity. The results are empirically validated on a real-world data consisting of about 6 million videos spread over 25 thousand channels. Chapter 4 considers the problem of scheduling advertisements in live personalized online social media. Broadcasters aim to opportunistically schedule advertisements (ads) so as to generate maximum revenue. The problem is formulated as a multiple stopping problem and is addressed in a partially observed Markov decision process (POMDP) framework. Structural results are provided on the optimal ad scheduling policy. By exploiting the structure of the optimal policy, optimum linear threshold policies are computed using a stochastic gradient algorithm. The proposed model and framework are validated on a Periscope dataset and it was found that the revenue can be improved by 25% in comparison to currently employed periodic scheduling.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat

The early origins of obesity: the importance of prenatal vs postnatal environment.

There is growing evidence that maternal obesity, maternal hyperglycemia or maternal intake of diets high in fat, sugar or total calories during pregnancy and lactation is associated with an increased risk of obesity and metabolic diseases in the offspring. The majority of studies to date, however, have examined the impact of maternal overnutrition during the entire perinatal period. While a small number of studies have provided clues that the impact of exposure to nutritional excess before birth in comparison to exposure during the early postnatal period may not be equivalent, the results of these studies have been inconsistent. Therefore, the relative contribution of prenatal and postnatal nutritional environment to obesity risk in the offspring remains unclear. The central aim of this thesis was to investigate the separate contributions of exposure to a maternal cafeteria diet during the prenatal and suckling periods on the metabolic outcomes of the offspring, specifically body weight, fat mass and the expression of key adipogenic and lipogenic genes at weaning, in early adolescence and in young adulthood using a cross-fostering approach in a rat model. The results of this thesis demonstrated that exposure to a maternal cafeteria diet during the suckling period is more important for determining fat mass at weaning than exposure before birth. Importantly, this thesis provided considerable evidence to suggest that exposure to a nutritionally-balanced diet during the suckling period has the capacity to prevent the negative effects of exposure to a high-fat/high-sugar diet before birth. In addition, this thesis has demonstrated that the effects of being exposed to a high-fat/high-sugar diet during the perinatal period on offspring adiposity could be reversed/controlled by consuming a nutritionally-balanced diet post-weaning. The results of this thesis also demonstrated that the levels of total fat, saturated and trans fats and omega-6 polyunsatured fatty acids (n-6 PUFA) in the dams milk were directly related to their levels in the maternal diet, and were higher in dams consuming a cafeteria diet. This supported the hypothesis that altered fat content and fatty acid composition of the milk is likely to play an important role in mediating the effects of maternal cafeteria diets on offspring fat mass, and may well account for the higher adiposity at weaning in offspring suckled by cafeteria-diet fed dams. Exposure to a cafeteria diet during the suckling period also resulted in altered expression of key adipogenic and lipogenic genes in visceral and subcutaneous fat depots and an increased susceptibility to diet-induced obesity in females. Importantly, this thesis provided evidence of clear sex-differences in the relative impact of prenatal and postnatal nutritional exposures on adipocyte gene expression and the susceptibility to diet-induced obesity in the offspring, suggesting that the timing of nutritional interventions aimed to re-program the offspring may be different in males and females. Overall, this thesis identifies the early postnatal period in rodents as a “critical window‟ for the programming of fat mass and susceptibility to diet-induced obesity in the offspring, and has provided important insights into the mechanisms underlying the early origins of obesity.Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food and Wine, 201

Dynamics of visons and thermal Hall effect in perturbed Kitaev models

A vison is an excitation of the Kitaev spin liquid which carries a $\mathbb Z_2$ gauge flux. While immobile in the pure Kitaev model, it becomes a dynamical degree of freedom in the presence of perturbations. We study an isolated vison in the isotropic Kitaev model perturbed by a small external magnetic field $h$, an offdiagonal exchange interactions $\Gamma$ and a Heisenberg coupling $J$. In the ferromagnetic Kitaev model, the dressed vison obtains a dispersion linear in $\Gamma$ and $h$ and a fully universal low-$T$ mobility, $\mu=6 v_m^2/T^{2}$, where $v_m$ is the velocity of Majorana fermions. In contrast, in the antiferromagnetic Kitaev model interference effects suppress the coherent propagation and an incoherent Majorana-assisted hopping leads to a $T$-independent mobility. The motion of a single vison due to Heisenberg interactions is strongly suppressed for both signs of the Kitaev coupling. Vison bands in the antiferromagnetic Kitaev models can be topological and may lead to a characteristic features in thermal Hall effects in Kitaev materials.Comment: 8+11 pages, 10 figures. Corrected an error in the field-induced vison hopping related to large finite size effects. Modified conclusions on Chern numbers and vison Hall effect. Added discussion on how vison diffusion acts as bottleneck for equilibratio

Optimal pricing in black box producer-consumer Stackelberg games using revealed preference feedback

This paper considers an optimal pricing problem for the black box producer-consumer Stackelberg game. A producer sets price over a set of goods to maximize profit (the difference in revenue and cost function). The consumer buys a quantity to maximize the difference between the value of the quantity consumed and the cost. The value function of the consumer and the cost function of the producer are ‘black box’ functions (unknown functions with limited or costly evaluations). Using Gaussian processes, Bayesian optimization and Bayesian quadrature we derive an algorithm for learning the optimal price. The method has the following significant advantages: (i) the method is efficient and scales well compared to existing techniques, (ii) the cost function of the producer could be non-convex, (iii) the value function and/or cost function can be time varying. We illustrate, using a real dataset, optimal pricing in electricity markets