Keep Moving: Stories

My thesis is a collection of short stories, all of which revolve around themes of travel, migration, and displacement. Ranging from short, one-page flash fiction pieces to longer stories, the six pieces in the collection are put together with the hope that they will thematically interact with each other and provide new meanings and interpretations for the whole collection, even as they continue to stand as distinct narratives in their own right. Through their interplay, I hope to throw light on the way human relationships are challenged by movement and migration

Élaboration et implantation du Guide d'Ottawa de médecine d'urgence

Implication Statement Medical students face multiple academic challenges during their transition to clerkship, including the ability to navigate various educational resources and translate acquired knowledge clinically. The Ottawa Handbook of Emergency Medicine (EM) was created by referencing EM textbooks and relevant literature, followed by a local peer-review process. A website metrics assessment was performed to assess student uptake. Implementation of the Ottawa Handbook of EM across Canadian clerkship curriculums is anticipated to bridge the EM knowledge gap for junior learners.  Énoncé des implications de la recherche Les étudiants en médecine sont confrontés à de multiples défis académiques au moment de leur transition vers l’externat, notamment à celui de se servir de diverses ressources éducatives et d'appliquer leurs connaissances dans un contexte clinique. Le Guide d’Ottawa de médecine d'urgence (MU) a été élaboré à partir de manuels de MU et de la littérature pertinente, et il a fait l'objet d'un processus local d'examen par les pairs. Une évaluation bibliométrique a été effectuée pour évaluer son utilisation par les étudiants. L’application du Guide d'Ottawa de médecine d'urgence dans le cadre des cursus canadiens d’externat devrait permettre de combler les lacunes qu’auraient les étudiants débutants en matière de médecine d'urgence

Achieving Software Release Management and Continuous Integration using Maven, Jenkins and Artifactory

It is often required that the developers working on a single module or software project, while working from remote locations. Scenarios like that increase complexity and challenge to manage releases and to merge their daily work in a single artifact. For component based development, developers are responsible for the merging their own work with others and to make sure the integrations went smooth with no error and problems. Sometimes it’s hard to do this job because of the large size of team members, big project size or geographically separated teams.  Through Continuous integration this whole process becomes lot easier because all the validations and verifications will be done by an automated job.  This job will check for the changes in the project code and whenever a change happens it will run the build process automatically, which detects errors and problems with the code and also notifies the concerned team member who is responsible for this error. This study introduces an approach to setup a Continuous Integrations techniques in a project using open source tools like Maven, Jenkins and Artifactory

Development of a robotic arm-gantry simulator with probabilistic inference based control

Robotic arms can perform repetitive tasks with high speed, accuracy, and precision, making them highly suitable for applications that are monotonous or require a high degree of precision. Such applications include industrial applications, human-robot collaboration applications, and experimental setups for model testing. A robust simulation as prototype testing is typically required to identify potential risks of catastrophic damage to the model or the experimental setup. Robot Operating System (ROS) is a widely-used framework for creating robotic applications both in research and in industry due to its easy hardware abstraction, code re-usability, and compatibility with popular open-source libraries. This thesis is focused on a robotic system consisting of a 7-DOF robotic arm ceiling mounted on a two-axis gantry. A simulator is developed for the 9-DOF robotic system in the ROS-Gazebo framework that interfaces with Moveit API for motion planning. A well-known Approximate Inference Control (AICO) algorithm is added to the simulator to extend its functionality to perform optimal trajectory planning. This work also extends the study of the AICO algorithm to non-holonomic mobile robots. Simulation experiments are conducted on both systems to study the behavior, performance, and limitations of AICO

Do Exchange Rates Fluctuations Influence Gold Price in G7 Countries? New Insights from a Nonparametric Causality-in-Quantiles Test

In the recent era, gold is considered an essential investment source, a source of hedging inflation, and a medium of monetary exchange. The gold and exchange rate nexus become prominent after events like sovereign debt crisis, subprime mortgage crisis, low-interest rate problem, and global financial market solvency. These events attract the attention of researchers and academician for investigating the dynamics of the relationship between gold and exchange rates, and the majority of the studies discusses the linear dynamics, but the non-linear dynamics are ignored. Therefore, the current research investigates the non-linear dynamics of gold price and exchange rate relationship in G7 countries using the new technique named the nonparametric causality approach. This study uses monthly data from the years 1995(January)-2017 (March). The empirical results show that exchange rate return causes gold prices in four out of G7, especially at the low tails. This study also gives valuable insights for monetary policymakers, gold exporter’s international portfolio managers, and hedge fund managers

Modeling Appropriate Language in Argumentation

Online discussion moderators must make ad-hoc decisions about whether the contributions of discussion participants are appropriate or should be removed to maintain civility. Existing research on offensive language and the resulting tools cover only one aspect among many involved in such decisions. The question of what is considered appropriate in a controversial discussion has not yet been systematically addressed. In this paper, we operationalize appropriate language in argumentation for the first time. In particular, we model appropriateness through the absence of flaws, grounded in research on argument quality assessment, especially in aspects from rhetoric. From these, we derive a new taxonomy of 14 dimensions that determine inappropriate language in online discussions. Building on three argument quality corpora, we then create a corpus of 2191 arguments annotated for the 14 dimensions. Empirical analyses support that the taxonomy covers the concept of appropriateness comprehensively, showing several plausible correlations with argument quality dimensions. Moreover, results of baseline approaches to assessing appropriateness suggest that all dimensions can be modeled computationally on the corpus

PromptIR: Prompting for All-in-One Blind Image Restoration

Image restoration involves recovering a high-quality clean image from its degraded version. Deep learning-based methods have significantly improved image restoration performance, however, they have limited generalization ability to different degradation types and levels. This restricts their real-world application since it requires training individual models for each specific degradation and knowing the input degradation type to apply the relevant model. We present a prompt-based learning approach, PromptIR, for All-In-One image restoration that can effectively restore images from various types and levels of degradation. In particular, our method uses prompts to encode degradation-specific information, which is then used to dynamically guide the restoration network. This allows our method to generalize to different degradation types and levels, while still achieving state-of-the-art results on image denoising, deraining, and dehazing. Overall, PromptIR offers a generic and efficient plugin module with few lightweight prompts that can be used to restore images of various types and levels of degradation with no prior information on the corruptions present in the image. Our code and pretrained models are available here: https://github.com/va1shn9v/PromptI

To Battle Income Inequality, Focus on Military Expenditures: Lesson from Pakistan

This study investigates the impact of military expenditures on income inequality in Pakistan using data over the period of 1972-2012. In doing so, we have applied the ARDL bounds testing cointegration approach which confirmed the presence of long run relationship between military expenditures and income inequality. Furthermore, empirical analysis indicates that military expenditures have positive impact on income inequality. The analysis of Granger causality, Toda and Yamamoto Modified Wald causality and variance decomposition approaches confirm the unidirectional causal relationship running from military expenditures to income inequality. The findings suggest that military expenditures would be a significant policy option to control income inequality and should be considered as a mean to improve income distribution in Pakistan

Citance-Contextualized Summarization of Scientific Papers

Current approaches to automatic summarization of scientific papers generate informative summaries in the form of abstracts. However, abstracts are not intended to show the relationship between a paper and the references cited in it. We propose a new contextualized summarization approach that can generate an informative summary conditioned on a given sentence containing the citation of a reference (a so-called "citance"). This summary outlines the content of the cited paper relevant to the citation location. Thus, our approach extracts and models the citances of a paper, retrieves relevant passages from cited papers, and generates abstractive summaries tailored to each citance. We evaluate our approach using $\textbf{Webis-Context-SciSumm-2023}$, a new dataset containing 540K~computer science papers and 4.6M~citances therein.Comment: Accepted at EMNLP 2023 Finding