Relationships between Currency Carry Trade and Stock Markets

My paper examines the relationship between currency carry trade and stock market returns. In this exercise, I analyze the effects of Japanese yen-based and US dollar-based carry trade strategies on the stock market performance of both funding and investment currencies. Currency-specific profit measure, calculated as the difference between future (realized) spot exchange rate and today forward rate, is used as a proxy for carry trade return. Using the traditional regression equation with explicitly accounting for GARCH effects in the error term, I find that: (1) there are positively significant associations between carry trade return and stock market performance in the corresponding target currency countries (Australia, New Zealand and China); (2) the relationship between carry trade and stock market returns in the corresponding funding currency countries (Japan and US) is mixed. There is negatively significant association between US dollar-based carry trade and US stock market while the relationship between yen-based carry trade and Japanese stock market is positive. My results raise a possible dispute on the role of Japanese yen as a popular choice of funding currency in carry trade transactions. However, the finding is well supported with robustness check by introducing two explanatory factors (control variables), namely market “fear gauge” VIX and Bloomberg Commodity Price indexes

Human-controllable and structured deep generative models

Deep generative models are a class of probabilistic models that attempts to learn the underlying data distribution. These models are usually trained in an unsupervised way and thus, do not require any labels. Generative models such as Variational Autoencoders and Generative Adversarial Networks have made astounding progress over the last years. These models have several benefits: eased sampling and evaluation, efficient learning of low-dimensional representations for downstream tasks, and better understanding through interpretable representations. However, even though the quality of these models has improved immensely, the ability to control their style and structure is limited. Structured and human-controllable representations of generative models are essential for human-machine interaction and other applications, including fairness, creativity, and entertainment. This thesis investigates learning human-controllable and structured representations with deep generative models. In particular, we focus on generative modelling of 2D images. For the first part, we focus on learning clustered representations. We propose semi-parametric hierarchical variational autoencoders to estimate the intensity of facial action units. The semi-parametric model forms a hybrid generative-discriminative model and leverages both parametric Variational Autoencoder and non-parametric Gaussian Process autoencoder. We show superior performance in comparison with existing facial action unit estimation approaches. Based on the results and analysis of the learned representation, we focus on learning Mixture-of-Gaussians representations in an autoencoding framework. We deviate from the conventional autoencoding framework and consider a regularized objective with the Cauchy-Schwarz divergence. The Cauchy-Schwarz divergence allows a closed-form solution for Mixture-of-Gaussian distributions and, thus, efficiently optimizing the autoencoding objective. We show that our model outperforms existing Variational Autoencoders in density estimation, clustering, and semi-supervised facial action detection. We focus on learning disentangled representations for conditional generation and fair facial attribute classification for the second part. Conditional image generation relies on the accessibility to large-scale annotated datasets. Nevertheless, the geometry of visual objects, such as in faces, cannot be learned implicitly and deteriorate image fidelity. We propose incorporating facial landmarks with a statistical shape model and a differentiable piecewise affine transformation to separate the representation for appearance and shape. The goal of incorporating facial landmarks is that generation is controlled and can separate different appearances and geometries. In our last work, we use weak supervision for disentangling groups of variations. Works on learning disentangled representation have been done in an unsupervised fashion. However, recent works have shown that learning disentangled representations is not identifiable without any inductive biases. Since then, there has been a shift towards weakly-supervised disentanglement learning. We investigate using regularization based on the Kullback-Leiber divergence to disentangle groups of variations. The goal is to have consistent and separated subspaces for different groups, e.g., for content-style learning. Our evaluation shows increased disentanglement abilities and competitive performance for image clustering and fair facial attribute classification with weak supervision compared to supervised and semi-supervised approaches.Open Acces

The sexually-transmitted Western Australia wild-plant virus yellow tailflower mild mottle virus: Does it pose a threat to global food security?

Yellow tailflower mild mottle virus is a species in the internationally-distributed genus Tobamovirus, other species of which are some of the most damaging plant viruses known. Yellow tailflower mild mottle virus (YTMMV) is the first tobamovirus described only from Australia and only from native plants. Because of the bad reputation of related tobamoviruses such as tobacco mosaic virus and cucumber green mottle mosaic virus as destroyers of valuable crops, we studied YTMMV to understand aspects of its biology and to assess its potential to spillover from the indigenous flora and threaten crops on national and international stages. Unlike many damaging plant viruses, tobamoviruses are not transmitted host-to-host by vectors such as aphids. Thus, understanding how YTMMV is transmitted between host plants is key to understanding aspects of its epidemiology. A further aim of our work was to assess the damage we might expect to see in some susceptible crops should YTMMV spillover

Optimal Number, Location, and Size of Distributed Generators in Distribution Systems by Symbiotic Organism Search Based Method

This paper proposes an approach based on the Symbiotic Organism Search (SOS) for optimal determining sizing, siting, and number of Distributed Generations (DG) in distribution systems. The objective of the problem is to minimize the power loss of the system subject to the equality and inequality constraints such as power balance, bus voltage limits, DG capacity limits, and DG penetration limit. The SOS approach is defined as the symbiotic relationship observed between two organisms in an ecosystem, which does not need the control parameters like other meta-heuristic algorithms in the literature. For the implementation of the proposed method to the problem, an integrated approach of Loss Sensitivity Factor (LSF) is used to determine the optimal location for installation of DG units, and SOS is used to find the optimal size of DG units. The proposed method has been tested on IEEE 33-bus, 69-bus, and 118-bus radial distribution systems. The obtained results from the SOS algorithm have been compared to those of other methods in the literature. The simulated results have demonstrated that the proposed SOS method has a very good performance and effectiveness for the problem of optimal placement of DG units in distribution systems

Using Cluster Analysis to Identify Subgroups of College Students at Increased Risk for Cardiovascular Disease

Background and Purpose: To examine the co-occurrence of cardiovascular risk factors and cluster subgroups of college students for cardiovascular risks. Methods: A cross sectional descriptive study was conducted using co-occurrence patterns and hierarchical clustering analysis in 158 college students. Results: The top co-occurring cardiovascular risk factors were overweight/obese and hypertension (10.8%, n = 17). Of the total 34 risk factors that co-occurred, 30 of them involved being overweight/obese. A six-cluster-solution was obtained, two clusters displayed elevated levels of lifetime and 30-year cardiovascular disease risks. Conclusions: The hierarchical cluster analysis identified that single White males with a family history of heart disease, overweight/obese, hypertensive or diabetes, and occasionally (weekly) consumed red meat, take antihypertensive medication, and hyperlipidemia were considered the higher risk group compared to other subgroups

Enterprise Systems and Customer Agility Exploratory Study in Vietnam

Contemporary companies in developing countries are raising their budget on Enterprise systems (ES). ES are expected to enhance Customer agility (CA) which refers to a firm’s capability to sense and respond to customer changes effectively. However, research in the relationship between ES on CA is contradictory. Taking Vietnam as the context, our study investigates the role of ES on CA in ten interviewed companies. Using multi-case methodology, the study also aims to seek for deep observation of CA in Vietnam business. Consistent with past literature, CA in the interviewed companies is affected by organizational context (i.e. industry, business function). In Vietnam, the role of ES on CA is not seen at its full extent. Most companies use ES to store and process data for its daily operation. Only a few of them store multiple types of data in their ES and utilize the advanced functions of the ES for customer analysis. Moreover, even when companies highly appreciate the competence of ES for advanced customer behaviour analysis, top managers are not using ES as a main source of information for their customer related decision-making. Due to their lack of trust on the capability of the system, low self-efficacy in ES usage plus the Vietnam trading specifics, top managers prefer other sources of data, own experiences and own report styles to make decisions

La relation entre flux d entrées nets et performance des fonds : une étude appliquée au cas des OPCVM actions français

Cet article étudie la relation entre les flux nets et la rentabilité relative des fonds actions français pendant la période 1992-2007. En utilisant la méthode des doubles clusters, on montre qu'il existe une relation convexe entre les flux nets et le rang de performance pour l'année précédente. Ainsi, au sein des fonds « stars » le rang de performance influence positivement l'attractivité du fonds, alors que pour les fonds de performance relative moyenne ou faible, il n'y a pas d'effet des performances passées. Nous montrons aussi que les calculs de rentabilité se fondent vraisemblablement sur des horizons courts. De plus comme dans le cas américain, la convexité est plus importante pour les « jeunes » fonds français. Toutefois, la relation est quantitativement et qualitativement moins marquée que dans le cas américain, ce qui traduit probablement les spécificités françaises en matière de distribution des fonds. Enfin, la convexité n'apparaît que dans les segments les moins spécialisés du marché (France et Europe) ce qui pourrait traduire le faible degré de sophistication de leur clientèle d'investisseurs.Fonds de placement

EFL STUDENTS’ PERCEPTIONS AND PRACTICES ON LEARNING STRATEGIES TOWARDS ESP FOR TOURISM AT SCHOOL OF SOCIAL SCIENCES AND HUMANITIES, CAN THO UNIVERSITY, VIETNAM

Learning strategies are determined as an effective approach to learning ESP. To implement more useful learning strategies, it is essential to understand the extent of practice toward learning strategies. The study focused on six (6) learning strategies, including (1) Memorization, (2) Cognitive, (3) Metacognitive, (4) Compensation, (5) Affective, and (6) Social, which were applied in four English skills belonging to English for Tourism courses. A hundred-item questionnaire was administered to investigate the perception and practice of learning strategies and interviews for students and teachers were employed to examine students’ practice of their involvement in learning strategies activities and discover teachers’ observations of students’ practice in their class. The study sample consists of 70 EFL (English as a foreign language) students who have taken the three courses of English for Tourism 1, 2, and 3 in their curriculum. The findings revealed that students had a positive attitude to a significant extent and students had positive perception toward their learning strategies performances. There found a medium correlation between the extent of perception impacts on students' practice that points out two hypotheses: (1) The higher the student's perception, the higher the extent of practice; (2) Students have good perception but the extent of practice does not depend on their perception. The findings may contribute to the discipline of ESP (English for Specific Purposes) since they not only help students understand the significance of learning strategies in learning ESP but also help ESP teachers understand students’ perceptions, from which teachers can generate more effective and appropriate learning strategies in classes for enhancing students’ productivity in learning ESP for Tourism.  Article visualizations

DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding

Human face exhibits an inherent hierarchy in its representations (i.e., holistic facial expressions can be encoded via a set of facial action units (AUs) and their intensity). Variational (deep) auto-encoders (VAE) have shown great results in unsupervised extraction of hierarchical latent representations from large amounts of image data, while being robust to noise and other undesired artifacts. Potentially, this makes VAEs a suitable approach for learning facial features for AU intensity estimation. Yet, most existing VAE-based methods apply classifiers learned separately from the encoded features. By contrast, the non-parametric (probabilistic) approaches, such as Gaussian Processes (GPs), typically outperform their parametric counterparts, but cannot deal easily with large amounts of data. To this end, we propose a novel VAE semi-parametric modeling framework, named DeepCoder, which combines the modeling power of parametric (convolutional) and nonparametric (ordinal GPs) VAEs, for joint learning of (1) latent representations at multiple levels in a task hierarchy1, and (2) classification of multiple ordinal outputs. We show on benchmark datasets for AU intensity estimation that the proposed DeepCoder outperforms the state-of-the-art approaches, and related VAEs and deep learning models.Comment: ICCV 2017 - accepte