Heavily separable functors of the second kind and applications

We introduce heavily separable functors of the second kind and study them in three different situations. The first of these is with restrictions and extensions of scalars for modules over small preadditive categories. The second is with free functors taking values in Eilenberg-Moore categories associated to a monad or a comonad. Finally, we consider entwined modules and give if and only if conditions for heavy separability of the second kind for functors forgetting either the comodule action or the module action

Empowering users to communicate their preferences to machine learning models in Visual Analytics

Recent visual analytic (VA) systems rely on machine learning (ML) to allow users to perform a variety of data analytic tasks, e.g., biologists clustering genome samples, medical practitioners predicting the diagnosis for a new patient, ML practitioners tuning models' hyperparameter settings, etc. These VA systems support interactive construction of models to people (I call them power users) with a diverse set of expertise in ML; from non-experts, to intermediates, to expert ML users. Through my research, I designed and developed VA systems for power users empowering them to communicate their preferences to interactively construct machine learning models for their analytical tasks. In this process, I design algorithms to incorporate user interaction data in machine learning modeling pipelines. Specifically, I deployed and tested (e.g., task completion times, user satisfaction ratings, success rate in finding user-preferred models, model accuracies) two main interaction techniques, multi-model steering, and interactive objective functions to facilitate specification of user goals and objectives to underlying model(s) in VA. However, designing these VA systems for power users poses various challenges, such as addressing diversity in user expertise, metric selection, user modeling to automatically infer preferences, evaluating the success of these systems, etc. Through this work I contribute a set of VA systems that support interactive construction and selection of supervised and unsupervised models using tabular data. In addition, I also present results/findings from a design study of interactive ML in a specific domain with real users and real data.Ph.D

Climate Change and the Response of Food Truck Industry: A Study on Vancouver Visitors’ Destination

Abstract Climate change is one of today’s major challenges to the tourism sector and its subsectors, such as the food truck industry. The purpose of this study is to determine how food trucks in Vancouver, Canada are responding to climate change. Previous literature on tourism and climate change have mainly focused on accommodation sectors, nature based tourism operators, and winter tourism activities. However, there is a dearth of empirical studies on climate change perceptions and adoption in food truck operations across North America especially from Vancouver context. Therefore, this study will explore to which extent food truck industries are aware of climate change issues in their day-to-day operations, and how they are strategically responding to the change. A qualitative approach (semi-structured interviews) will be employed to collect data from a sample of food truck operators in Vancouver, one of the most popular visitor destinations in Canada

On the generalized weighted Sobolev inequality

Let $\Omega$ be an open subset of $\mathbb{R}^N.$ We identify various classes of Young functions $\Phi,\,\Psi$, and weight functions $g\in L^1_\text{loc}(\Omega)$ so that the following generalized weighted Sobolev inequality holds: \begin{equation*}\label{ineq:Orlicz} \Psi^{-1}\left(\int_{\Omega}|g(x)|\Psi( |u(x)| )dx \right)\leq C\Phi^{-1}\left(\int_{\Omega}\Phi(|\nabla u(x)|) dx \right),\,\,\,\forall\,u\in \mathcal{C}^1_c(\Omega), \end{equation*} for some $C>0$. As an application, we study the existence of non-negative solutions for certain nonlinear weighted eigenvalue problems.Comment: 27 page