Maximum A Posteriori Inference in Sum-Product Networks

Sum-product networks (SPNs) are a class of probabilistic graphical models that allow tractable marginal inference. However, the maximum a posteriori (MAP) inference in SPNs is NP-hard. We investigate MAP inference in SPNs from both theoretical and algorithmic perspectives. For the theoretical part, we reduce general MAP inference to its special case without evidence and hidden variables; we also show that it is NP-hard to approximate the MAP problem to $2^{n^\epsilon}$ for fixed $0 \leq \epsilon < 1$, where $n$ is the input size. For the algorithmic part, we first present an exact MAP solver that runs reasonably fast and could handle SPNs with up to 1k variables and 150k arcs in our experiments. We then present a new approximate MAP solver with a good balance between speed and accuracy, and our comprehensive experiments on real-world datasets show that it has better overall performance than existing approximate solvers

Modeling and control to improve blood glucose concentration for people with diabetes

Diabetes mellitus is a chronical condition that features either the lack of insulin or increased insulin resistance. It is a disorder in the human metabolic system. To combat insufficiency of insulin released by pancreas, a closed-loop control system, also known as artificial pancreas (AP) in this application, have been created to mimic the functionality of a human pancreas. An AP is used to regulate blood glucose concentration (BGC) by managing the release of insulin. Therefore, an algorithm, which can administer insulin to reduce the variation of BGC and minimize the occurrences of hyper-/ hypoglycemia episodes, is the key component of an AP. The objective of the dissertation is to develop an optimal algorithm to better control BGC for people with diabetes. For people with Type 2 diabetes, prevention or treatment of diabetes mellitus can typically be done via a change of lifestyle and weight management. A virtual sensing system that does not require many manual inputs from patients can ease the burden for people with Type 2 diabetes. This dissertation covers the development of a monitoring system for Type 2 diabetes. To achieve the goal of tighter control of BGC for people with Type 1 diabetes, dynamic modeling methodology for capturing the cause-and-effect relationship between manipulated variable (i.e. insulin) and controlled variable (i.e. BGC) has been developed. Theoretically, this dissertation has established that physiologically based nonlinear parameterized wiener models being superior to nonlinear autoregressive moving average with exogenous inputs (NARMAX) models in capturing dynamic relationships in processes with correlated inputs. Based on these results, wiener models have been applied in the modeling of BGC for real subjects with Type 1 diabetes under free-living conditions. With promising results shown in wiener models, an extended physiologically based model (i.e. semi-coupled model) has been developed from wiener structure, which enables the development of a phenomenologically sound feedforward control law. The feedforward control law based on wiener models has been tested in simulated continuous-stirred-tank reactor (CSTR) that demonstrates tight control of controlled variables. Further simulation runs with a CSTR also shows feedforward predictive control (FFPC) can provide tighter control over model predictive control (MPC). Lastly, for the special application of BGC control for people with Type 1 diabetes, FFPC demonstrates tighter control than MPC under simulation environment. To account for unmeasured disturbances and inaccurate models for manipulated variable in real life scenarios, feedback predictive control (FBPC) is developed and proven to be a more effective control algorithm under both CSTR and diabetes simulation environment, which can establish the foundation for tightening BGC in real subject clinical studies

Group dynamics in ESL collaborative academic writing

Collaborative writing involves group members engaging directly with one another to complete a task. The quality of learning from members is closely associated with the nature of the collaboration and the interactions that take place. This paper examines the dynamics of three groups of tertiary ESL learners as they collaborated on three expository essays. Data were collected from nine audio-recordings of group discussions and four semi-structured interviews. The three cases unveiled very different dynamics due to group connection, individual traits, level of engagement, and degree of control. Social, affective and cognitive processes affected the direction and the quality of collaboration. With an understanding of the group dynamics that ensues during collaboration, practitioners will be better prepared when incorporating collaboration in their writing class

Improving ESL learners' academic text construction through a collaborative task

Many ESL writing instructors incorporate some form of pair or group work at some stage of the writing process to provide their students the opportunity to brainstorm ideas, plan, and co-construct knowledge with their peers. Another reason is to encourage students to work independently from the teacher with the intention that they will be more autonomous. This paper reports on a qualitative study which examines how one group of tertiary ESL learners in an academic writing course jointly produce an academic essay during one collaborative task. This study highlights some critical incidents pertaining to the students' roles, their behaviours, and instances that contribute to knowledge and text construction during the group work. The findings showed that cumulative talk and use of questions moved the group discussion forward while negotiation helped the learners to test ideas at a deeper level. The learners also shared their expertise during text construction. The affective conflict which the group encountered during the collaboration also helped them to deal with differing viewpoints and maintaining coherence in the group