An Interview with Joy Priest

Joy Priest is the author of HORSEPOWER (Pitt Poetry Series, 2020), winner of the Donald Hall Prize for Poetry. She is the recipient of a 2021 NEA fellow- ship and a 2019-2020 Fine Arts Work Center fellowship, and has won the 2020 Stanley Kunitz Memorial Prize from APR, and the Gearhart Poetry Prize from The Southeast Review. Her poems have appeared in the Academy of American Poets’ Poem-a-Day, The Atlantic, and Virginia Quarterly Review, among others. Her essays have appeared in The Bitter Southerner, Poets & Writers, ESPN, and The Undefeated, and her work has been anthologized in Breakbeat Poets: New American Poetry in the Age of Hip-Hop, The Louisville Anthology, A Measure of Belonging: Writers of Color on the New American South, and Best New Po- ets 2014, 2016 and 2019. Joy received her M.F.A. in poetry, with a certificate in Women & Gender Studies from the University of South Carolina. She is currently a doctoral student in Literature & Creative Writing at the University of Houston.

Deep Residual Involution Network for Hyperspectral Image Classification

Convolutional neural networks (CNNs) have achieved great results in hyperspectral image (HSI) classification in recent years. However, convolution kernels are reused among different spatial locations, known as spatial-agnostic or weight-sharing kernels. Furthermore, the preference of spatial compactness in convolution (typically, 3×3 kernel size) constrains the receptive field and the ability to capture long-range spatial interactions. To mitigate the above two issues, in this article, we combine a novel operation called involution with residual learning and develop a new deep residual involution network (DRIN) for HSI classification. The proposed DRIN could model long-range spatial interactions well by adopting enlarged involution kernels and realize feature learning in a fairly lightweight manner. Moreover, the vast and dynamic involution kernels are distinct over different spatial positions, which could prioritize the informative visual patterns in the spatial domain according to the spectral information of the target pixel. The proposed DRIN achieves better classification results when compared with both traditional machine learning-based and convolution-based methods on four HSI datasets. Especially in comparison with the convolutional baseline model, i.e., deep residual network (DRN), our involution-powered DRIN model increases the overall classification accuracy by 0.5%, 1.3%, 0.4%, and 2.3% on the University of Pavia, the University of Houston, the Salinas Valley, and the recently released HyRANK HSI benchmark datasets, respectively, demonstrating the potential of involution for HSI classification.

MOOCs and Open Education in the Global South: A Review

This timely and eye-opening book from Ke Zhang, Curt Bonk, Tom Reeves, and Tom Reynolds, MOOCs and Open Education in the Global South (Zhang, Bonk, Reeves, & Reynolds, 2020), provides 28 chapters that describe the challenges, successes, and opportunities of MOOCs and open education from the perspective of 68 authors from 47 countries in the Global South (http://moocsbook.com). Before those chapters, a detailed preface from the four editors lays out the journey that the world community took to get to this point in the metaphor of a wanderer who makes his or her path by pushing ahead and exploring the road in front. In addition, an insightful foreword is provided by Mimi Miyoung Lee from the University of Houston who had previously co-edited an award-winning book with Bonk, Reeves, and Reynolds; namely, MOOCs and Open Education Around the World (Bonk, Lee, Reeves, & Reynolds, 2015). Thus, consider the current book Part 2 of what is likely to become a many act play in the world of MOOCs and open education. With the foreword and preface, there are 30 pieces in total (Note: the front matter is available for free from: http://moocsbook.com/MOOCs_Open-Ed_Global-South-frontmatter_2020_Zhang_Bonk_Reeves_Reynolds.pdf).

“UHAND”—A National Cancer Institute Funded Partnership to Advance Cancer Health Equity through Scholar Training

Black and Hispanic adults are disproportionately affected by cancer incidence and mortality, and experience disparities in cancer relative to their White counterparts in the US. These groups, including women, are underrepresented among scientists in the fields of cancer, cancer disparities, and cancer care. The “UHAND” Program is a partnership between institutions (University of Houston and The University of Texas MD Anderson Cancer Center) aiming to build the capacity of underrepresented and racial/ethnic minority student “scholars” to conduct research on eliminating cancer inequities by reducing social and physical risk factors among at-risk groups. Here, we examine the outcomes of the UHAND Program’s first scholar cohort (n = 1 postdoctoral fellow, n = 3 doctoral scholars, n = 6 undergraduate scholars). Data collection included baseline, mid-program, and exit surveys; program records; and monthly scholar achievement queries. From baseline to exit, scholars significantly increased their research self-efficacy (p = 0.0293). Scholars largely met goals for academic products, achieving a combined total of 65 peer-reviewed presentations and nine empirical publications. Eight scholars completed the 2-year program; one undergraduate scholar received her degree early and the postdoctoral fellow accepted a tenure-track position at another university following one year of training. Scholars highly rated UHAND’s programming and their mentors’ competencies in training scholars for research careers. Additionally, we discuss lessons learned that may inform future training programs.

Well-Flux Surveillance and Ramp-Up Method for Openhole Standalone Screen Completion

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 201662, “A Well-Flux Surveillance and Ramp-Up Method for Openhole Standalone Screen Completion,” by Mehmet Karaaslan and George K. Wong, University of Houston, and Kevin L. Soter, SPE, Shell, et al., prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, Colorado, 5-7 October. The paper has not been peer reviewed. Production and surveillance engineers need practical models to help maximize production while avoiding ramping up the well to an extent that the completion is damaged, causing well impairment or failure. The complete paper presents a well-flux surveillance method to monitor and ramp up production for openhole standalone screen (OH-SAS) completions that optimizes production by considering risks of production impairment and screen-erosion failure. Challenges of Increased Production vs. Well Failure The problem of increased production vs. the risk of well impairment or failure is a pressing problem for sand-control wells in deepwater, where projects tend to have a small number of high-rate wells. In such environments, any well impairments or failures greatly affect the project economics. Following unloading, well surveillance faces the critical step of ramping up to-ward the well’s designed peak rate for the first time when the actual well performance is uncertain. To reduce risk of well impairment or failure, surveillance information and models are needed to help make adjustments during the ramp-up process. Different models are available, from simple to complex and from small to large amounts of input data and computational efforts. Simple nonsurveillance models use field-derived operating limits of completion pressure drop and flow velocity or flux. They are non-surveillance models in the sense that no direct linkage of surveillance results to update flux calculations exists. Simple surveillance models use pressure transient analysis (PTA) results and completion information to evaluate changing well performance and adjust the ramp-up and long-term surveillance operations. The complex surveillance model evaluates well performance and adjusts well operations using probabilistic completion failure risks and coupled reservoir and completion simulations. These models mainly focus on cased-hole gravel pack and frac-pack applications. For openhole completions with sand control, the literature offers limited ramp-up surveillance references. The objective of the well-flux model described in the complete paper is to ramp up the well safely and optimize production using PTA results as surveillance inputs to calculate completion fluxes for well impairment or failure assessment. The method follows an approach presented in the literature.

Combating Procrastination on Massive Online Open Courses via Optimal Calls to Action

Massive online open courses (MOOCs) are a booming phenomenon in the digital era. However, the online nature of educational delivery via MOOCs creates every opportunity for digital distraction and procrastination, resulting in difficulties for students and instructors. According to a new study in Information Systems Research, the authors Ni Huang (University of Houston), Jiayin Zhang (Tsinghua University), Gordon Burtch (University of Minnesota), Xitong Li (HEC Paris), and Peiyu Chen (Arizona State University) report a randomized field experiment on a large MOOC platform to examine several calls to action (CTAs) pertaining to the completion and submission of course assignments with an eye toward combating student procrastination on MOOCs. Their results show that descriptive norms (i.e., informing the completion rates of the assignments) lead to higher probabilities of assignment completion and a shorter time to completion. In contrast, a deadline reminder in the form of a planning prompt (i.e., informing the target deadline for assignment submission and the importance of planning ahead) has a surprisingly counterproductive effect, in particular, if students’ active course load is low. One possible explanation is that the students with low course loads may perceive the deadline to be distant, which reduces their sense of urgency and leads to complacency.