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Volume 32, Number 3, Summer/Fall 2023, Office Of Communications, Illinois Wesleyan University

Illinois Wesleyan University Magazine, 2018-present

VOLUME 32 | NUMBER 3 | Summer/Fall 2023

ON OUR COVER:

Brenda Lessen Knoll '89 performs her physical therapy treatment, PIOMI, with 12-hour old premature infant Remi Audeman.

14 Handmade Miracles Brenda Lessen Knoll '89 was completing her doctorate when she developed a groundbreaking new physical therapy. Now it's implemented around the world to help premature infants grow using only caregiver's hands.

18 A Chance to Cure Dr. Ann Stroink '76 winner of the 2023 Distinguished Alumni Award, has mastered one of medicine's most demanding fields -- neurosurgery. She has saved lives and trained a new generation of surgeons, for …

Kinetic Particle Simulations Of Plasma Charging At Lunar Craters Under Severe Conditions, David Lund, Xiaoming He, Daoru Frank Han

Mathematics and Statistics Faculty Research & Creative Works

This paper presents fully kinetic particle simulations of plasma charging at lunar craters with the presence of lunar lander modules using the recently developed Parallel Immersed-Finite-Element Particle-in-Cell (PIFE-PIC) code. The computation model explicitly includes the lunar regolith layer on top of the lunar bedrock, taking into account the regolith layer thickness and permittivity as well as the lunar lander module in the simulation domain, resolving a nontrivial surface terrain or lunar lander configuration. Simulations were carried out to study the lunar surface and lunar lander module charging near craters at the lunar terminator region under mean and severe plasma environments. …

Marketing In Metaverse: Sales-Avatar Visual Features On Customer Trust, Yuxin Liu, Keng Siau

Research Collection School Of Computing and Information Systems

The metaverse wave is sweeping across the world. Virtual shopping promises a bright future and has the potential to provide customers with an immersive and realistic shopping experience anytime and anywhere. Salesavatars are critical in customer trust development in virtual shopping in the metaverse by directly interacting with customers. Considering the virtual characteristics of the metaverse and the anonymity of salesavatars, this research investigates the effect of salesavatars' genders and ages on customers' trust in the metaverse through the lens of first impression formation. Two dimensions of facial first impression, perceived facial trustworthiness and perceived facial dominance, will be studied …

Estimation Of Recursive Route Choice Models With Incomplete Trip Observations, Tien Mai, The Viet Bui, Quoc Phong Nguyen, Tho V. Le

Research Collection School Of Computing and Information Systems

This work concerns the estimation of recursive route choice models in the situation that the trip observations are incomplete, i.e., there are unconnected links (or nodes) in the observations. A direct approach to handle this issue could be intractable because enumerating all paths between unconnected links (or nodes) in a real network is typically not possible. We exploit an expectation–maximization (EM) method that allows dealing with the missing-data issue by alternatively performing two steps of sampling the missing segments in the observations and solving maximum likelihood estimation problems. Moreover, observing that the EM method could be expensive, we propose a …

Machine-Learning Approach To Automated Doubt Identification On Stack Overflow Comments To Guide Programming Learners, Tianhao Chen, Eng Lieh Ouh, Kar Way Tan, Siaw Ling Lo

Research Collection School Of Computing and Information Systems

Stack Overflow is a popular Q&A platform for developers to find solutions to programming problems. However, due to the varying quality of user-generated answers, there is a need for ways to help users find high-quality answers. While Stack Overflow's community-based approach can be effective, important technical aspects of the answer need to be captured, and users’ comments might contain doubts regarding these aspects. In this paper, we showed the feasibility of using a machine learning model to identify doubts and conducted data analysis. We found that highly reputed users tend to raise more doubts; most answers have doubt in the …

Conference Report On 2022 Ieee Symposium Series On Computational Intelligence (Ieee Ssci 2022), Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao

Research Collection School Of Computing and Information Systems

On behalf of the organizing committee, we are delighted to deliver this conference report for the 2022 IEEE Symposium Series on Computational Intelligence (SSCI 2022), which was held in Singapore from 4th to 7th December 2022. IEEE SSCI is an established flagship annual international series of symposia on computational intelligence (CI) sponsored by the IEEE Computational Intelligence Society (CIS) to promote and stimulate discussions on the latest theory, algorithms, applications, and emerging topics on computational intelligence. After two years of virtual conferences due to the global pandemic, IEEE SSCI returned as an in-person meeting with online elements in 2022.

Discriminative Reasoning With Sparse Event Representation For Document-Level Event-Event Relation Extraction, Changsen Yuan, Heyan Huang, Yixin Cao, Yonggang Wen

Research Collection School Of Computing and Information Systems

Document-level Event-Event Relation Extraction (DERE) aims to extract relations between events in a document. It challenges conventional sentence-level task (SERE) with difficult long-text understanding. In this paper, we propose a novel DERE model (SENDIR) for better document-level reasoning. Different from existing works that build an event graph via linguistic tools, SENDIR does not require any prior knowledge. The basic idea is to discriminate event pairs in the same sentence or span multiple sentences by assuming their different information density: 1) low density in the document suggests sparse attention to skip irrelevant information. Our module 1 designs various types of attention …

Large-Scale Correlation Analysis Of Automated Metrics For Topic Models, Jia Peng Lim, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Automated coherence metrics constitute an important and popular way to evaluate topic models. Previous works present a mixed picture of their presumed correlation with human judgement. In this paper, we conduct a large-scale correlation analysis of coherence metrics. We propose a novel sampling approach to mine topics for the purpose of metric evaluation, and conduct the analysis via three large corpora showing that certain automated coherence metrics are correlated. Moreover, we extend the analysis to measure topical differences between corpora. Lastly, we examine the reliability of human judgement by conducting an extensive user study, which is designed as an amalgamation …

Diaasq: A Benchmark Of Conversational Aspect-Based Sentiment Quadruple Analysis, Bobo Li, Hao Fei, Fei Li, Yuhan Wu, Jinsong Zhang, Shengqiong Wu, Jingye Li, Yijiang Liu, Lizi Liao, Tat-Seng Chua, Donghong Ji

Research Collection School Of Computing and Information Systems

The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society. The current ABSA works, however, are mostly limited to the scenario of a single text piece, leaving the study in dialogue contexts unexplored. To bridge the gap between fine-grained sentiment analysis and conversational opinion mining, in this work, we introduce a novel task of conversational aspect-based sentiment quadruple analysis, namely DiaASQ, aiming to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. We manually construct a large-scale high-quality DiaASQ dataset in both Chinese and English languages. We deliberately develop a neural model to benchmark …

Multi-Target Backdoor Attacks For Code Pre-Trained Models, Yanzhou Li, Shangqing Liu, Kangjie Chen, Xiaofei Xie, Tianwei Zhang, Yang Liu

Research Collection School Of Computing and Information Systems

Backdoor attacks for neural code models have gained considerable attention due to the advancement of code intelligence. However, most existing works insert triggers into task-specific data for code-related downstream tasks, thereby limiting the scope of attacks. Moreover, the majority of attacks for pre-trained models are designed for understanding tasks. In this paper, we propose task-agnostic backdoor attacks for code pre-trained models. Our backdoored model is pre-trained with two learning strategies (i.e., Poisoned Seq2Seq learning and token representation learning) to support the multi-target attack of downstream code understanding and generation tasks. During the deployment phase, the implanted backdoors in the victim …

Safe Mdp Planning By Learning Temporal Patterns Of Undesirable Trajectories And Averting Negative Side Effects, Siow Meng Low, Akshat Kumar, Scott Sanner

Research Collection School Of Computing and Information Systems

In safe MDP planning, a cost function based on the current state and action is often used to specify safety aspects. In real world, often the state representation used may lack sufficient fidelity to specify such safety constraints. Operating based on an incomplete model can often produce unintended negative side effects (NSEs). To address these challenges, first, we associate safety signals with state-action trajectories (rather than just immediate state-action). This makes our safety model highly general. We also assume categorical safety labels are given for different trajectories, rather than a numerical cost function, which is harder to specify by the …

The Internet Of Things (Iot) In Healthcare: Taking Stock And Moving Forward, Abderahman Rejeb, Karim Rejeb, Horst Treiblmaier, Andrea Appolloni, Salem Alghamdi, Yaser Alhasawi, Mohammad Iranmanesh

Research outputs 2022 to 2026

Recent improvements in the Internet of Things (IoT) have allowed healthcare to evolve rapidly. This article summarizes previous studies on IoT applications in healthcare. A comprehensive review and a bibliometric analysis were performed to objectively summarize the growth of IoT research in healthcare. To begin, 2,990 journal articles were carefully selected for further investigation. These publications were analyzed based on various bibliometric metrics, including publication year, journals, authors, institutions, and countries. Keyword co-occurrence and co-citation networks were generated to unravel significant research hotspots. The findings show that IoT research has received considerable interest from the healthcare community. Based on the …

Mechanical Properties And Deformation Mechanisms Of Nanocrystalline U-10mo Alloys By Molecular Dynamics Simulation, Xuelian Ou, Yanxin Shen, Yue Yang, Zhenjiang You, Peng Wang, Yexin Yang, Xiaofeng Tian

Research outputs 2022 to 2026

U-Mo alloys were considered to be the most promising candidates for high-density nuclear fuel. The uniaxial tensile behavior of nanocrystalline U-10Mo alloys with average grain sizes of 8–23 nm was systematically studied by molecular dynamics (MD) simulation, mainly focusing on the influence of average grain size on the mechanical properties and deformation mechanisms. The results show that Young’s modulus, yield strength and ultimate tensile strength follow as average grain size increases. During the deformation process, localized phase transitions were observed in samples. Grain boundary sliding and grain rotation, as well as twinning, dominated the deformation in the smaller and larger …

Survey: An Overview Of Lightweight Rfid Authentication Protocols Suitable For The Maritime Internet Of Things, Glen Mudra, Hui Cui, Michael N. Johnstone

Research outputs 2022 to 2026

The maritime sector employs the Internet of Things (IoT) to exploit many of its benefits to maintain a competitive advantage and keep up with the growing demands of the global economy. The maritime IoT (MIoT) not only inherits similar security threats as the general IoT, it also faces cyber threats that do not exist in the traditional IoT due to factors such as the support for long-distance communication and low-bandwidth connectivity. Therefore, the MIoT presents a significant concern for the sustainability and security of the maritime industry, as a successful cyber attack can be detrimental to national security and have …

Confirmatory Factor Analysis Of Two Self-Efficacy Scales For Astronomy Understanding And Robotic Telescope Use, R. Freed, David H. Mckinnon, M. T. Fitzgerald, S. Salimpour

Research outputs 2022 to 2026

This paper presents the results of a confirmatory factor analysis on two self-efficacy scales designed to probe the self-efficacy of college-level introductory astronomy (Astro-101) students (n=15181) from 22 institutions across the United States of America and Canada. The students undertook a course based on similar curriculum materials, which involved students using robotic telescopes to support their learning of astronomical concepts covered in the "traditional"Astro-101 courses. Previous research by the authors using these self-efficacy scales within a pre-/post-test approach showed both high reliabilities and very high construct validities. However, the scale purporting to measure students' self-efficacy in relation to their use …

Memory-Multi-Fractional Brownian Motion With Continuous Correlations, Wei Wang, Michał Balcerek, Krzysztof Burnecki, Aleksei V. Chechkin, Skirmantas Janušonis, Jakub Ślȩzak, Thomas Vojta, Agnieszka Wyłomańska, Ralf Metzler

Physics Faculty Research & Creative Works

We propose a generalization of the widely used fractional Brownian motion (FBM), memory-multi-FBM (MMFBM), to describe viscoelastic or persistent anomalous diffusion with time-dependent memory exponent α(t) in a changing environment. In MMFBM the built-in, long-range memory is continuously modulated by α(t). We derive the essential statistical properties of MMFBM such as its response function, mean-squared displacement (MSD), autocovariance function, and Gaussian distribution. In contrast to existing forms of FBM with time-varying memory exponents but a reset memory structure, the instantaneous dynamic of MMFBM is influenced by the process history, e.g., we show that after a steplike change of α(t) the …

Oxygen And Hydrogen Isotopic Evidence That KamaʻEhuakanaloa (LōʻIhi) Seamount Hydrothermal Systems Are Recharged By Deep Pacific Seawater, Eric W. Chan, Brianna A. Alanis, Christopher R. German, Darlene S. S. Lim, John A. Breier

School of Earth, Environmental, & Marine Sciences Faculty Publications

We observed negative and positive δ18O and δ2H deviations from fluids collected at Kamaʻehuakanaloa (previously known as Lōʻihi) seamount relative to Pacific seawater from the same depth. Hydrothermal vents on the crater floor of Pele's Pit at Kamaʻehuakanaloa, at a depth of 1320 m, had δ18O and δ2H values as low as −0.19‰ and −0.3‰, respectively. Seawater collected within the caldera, within a zone 45 m above the crater floor, had δ18O and δ2H values as high as 1.15‰ and 6.5‰, respectively. In comparison, Pacific seawater at 1200 m and 1400 m at nearby station ALOHA exhibit intermediate δ18O and …

Life Cycle Assessment Of Calcium Oxide Pretreatment Of Corn Stover With Carbon Dioxide Neutralization For Ethanol Production, Jikai Zhao, Danyi Feng, Juhee Lee

School of Earth, Environmental, & Marine Sciences Faculty Publications

This work used life-cycle assessment (LCA) to determine the environmental and human health impacts of four ethanol production scenarios (S1: CaO pretreatment + H2SO4 neutralization + C6 yeast fermentation; S2: CaO pretreatment + CO2 neutralization + C6 yeast fermentation; S3: CaO pretreatment + H2SO4 neutralization + C6/C5 yeast fermentation; and S4: CaO pretreatment + CO2 neutralization + C6/C5 yeast fermentation), with the functional unit being 1 kg of 95 % ethanol. The LCA results showed that the total ozone depletion, global warming potential, smog, acidification, eutrophication, and ecotoxicity values were comparable when CO2 or H2SO4 were used to adjust the …

Cost Estimation Trends For Major Defense Acquisition Programs, Sammantha Jones, Edward D. White, Jonathan D. Ritschel, Shawn M. Valentine

Faculty Publications

The authors use both descriptive and inferential techniques to investigate average and standard deviation trends in cost estimates for major defense acquisition programs (MDAPs) grouped into decades from the 1970s to 2010s. For total program-cost-growth factors (CGFs), the 2010s exhibited lower CGFs compared to the 1990s. For the program-acquisition-unit cost (PAUC) CGFs, the 2010s appear lower than the 1990s and borderline lower than the 1970s. A statistically significant decreasing trend in the standard deviations of total program CGFs throughout the decades was identified. This lowering variability trend also appeared for PAUC CGFs from the 1980s onward. This finding appears to …

Bidiagonal Decompositions Of Vandermonde-Type Matrices Of Arbitrary Rank, Jorge Delgado, Plamen Koev, Ana Marco, José Javier Martínez, Juan Manuel Peña, Per Olof Persson, Steven Spasov

Faculty Research, Scholarly, and Creative Activity

We present a method to derive new explicit expressions for bidiagonal decompositions of Vandermonde and related matrices such as the (q-, h-) Bernstein-Vandermonde ones, among others. These results generalize the existing expressions for nonsingular matrices to matrices of arbitrary rank. For totally nonnegative matrices of the above classes, the new decompositions can be computed efficiently and to high relative accuracy componentwise in floating point arithmetic. In turn, matrix computations (e.g., eigenvalue computation) can also be performed efficiently and to high relative accuracy.

The Finance And Economics Women’S Network (Few): Encouraging And Engaging Women In Undergraduate Programs, Joy Buchanan, Darwyyn Deyo

Faculty Research, Scholarly, and Creative Activity

We describe a novel intercollegiate resource for engaging more undergraduate women in finance and economics programs. In response to the gender gap in undergraduate studies, we developed the Finance and Economics Women’s Network (FEW) to support women majoring in finance and economics through FEW clubs. These clubs provide professional skill development and visibility for women in these professions through speaker events. Students run FEW clubs, and faculty advisors and community professionals support them. We also conducted a descriptive survey of students and report the responses. Finally, we provide a constitution template to facilitate the creation of FEW clubs.

Modeling Cyanotoxin Production, Fate, And Transport In Surface Water Bodies Using Ce-Qual-W2, Bernadel Rose Hintz Garstecki, Scott A. Wells

Civil and Environmental Engineering Faculty Publications and Presentations

Cyanobacteria are frequently associated with forming toxic blooms. The toxins produced by cyanobacteria, cyanotoxins, are harmful to both humans and animals. Rising temperatures due to global climate change are expected to increase the occurrence of cyanobacteria, and it is vital that we protect our drinking water supplies and natural water resources. Modeling the production, fate, and transport of these toxins is an important step in limiting exposure to them and evaluating management strategies to mitigate their impact. The research provided here offers an overview of some of the main cyanotoxins of concern and presents preliminary models for the transport and …

How Equitable Teaching Practices Impact The Mathematical Identity Of English Language Learners From Teachers’ Perspectives, Xiaomei Segelstrom

School of Education and Leadership Student Capstone Theses and Dissertations

This mixed method research study examined the teacher’s perspectives on how equitable teaching practices impact the math identity of English language learners. A quantitative research survey instrument including five major themes that was generated from the frameworks in the literature review was used, and then followed by semi-structured interviews to address the research question. The data collected from the survey indicated that participants do believe that equitable teaching practices would impact the math identity of ELLs via: conveying high expectations, language support, modifying lessons, mathematical tasks and mathematical tools. Building on the survey results, interviewees furthermore informed that all these …

Applying Homomorphic Encryption To A Cross Domain Problem, Cheyenne Dailey

Theses

The Cross Domain Problem (CDP) strives to ensure protected data transference across varying security domains. In order to accomplish this, a Cross Domain Solution (CDS) is needed. A common method to protect data is to focus on risk management between trusted parties; however, untrusted parties pose ongoing concern. The problem is determining a method that transfers classified data through various security domains without exposing any information to intermediary parties. Attempts to mitigate this problem have been made utilizing Homomorphic Encryption (HE), a type of encryption that allows for computations to be executed on encrypted data without needing to decrypt it. …

Continual Domain Adaptation Through Knowledge Distillation, Georgi Thomas

Theses

Domain Adaptation (DA) techniques aim to overcome the domain shift between a source domain used for training and the target domain used for testing and deployment. Domain adaptation methods assume the entire target domain is accessible during the adaptation process. We use efficient architectures in the continual data-constrained DA paradigm where the unlabeled data in the target domain is received continually in batches. In recent years, Vision Transformers have emerged as an alternative to traditional Convolutional Neural Networks (CNNs) as the feature extraction backbone for image classification and other computer vision tasks. Within the field of DA, these attention-based architectures …

Diffractive Light And Solar Sails, Prateek Ranjan Srivastava

Theses

In the realm of space exploration, humanity has achieved remarkable feats like landing humans on the moon. However, our progress in venturing beyond our solar system has been limited. Light sails, which utilize photon momentum from laser or solar radiation, o↵er a promising propulsion technology. Unlike traditional rockets, light sails carry no fuel and can achieve high velocities and precise orbital maneuvers. In this research, we propose using an elementary space variant di↵ractive film to design passively stable light sails propelled by laser radiation. We also explore applying this concept to solar sails, enabling spiral trajectories and high orbital inclination …

A Privacy Safeguarding Framework For The Smart Grid, Gaurav Shivaji Wagh

Theses

The smart grid is an outcome of integrating communication technologies with traditional electrical systems. This enables the collection of granular metering data from the customer domain for providing grid and billing functionalities. However, the data collection process exposes the grid to various cyberattacks, posing a significant threat to customer privacy. This is a critical concern for the smart grid community and has hindered the global adoption of the smart grid technology. Although aggregation-based frameworks show promise for sharing metering data with the Electrical Service Provider while maintaining customer privacy, existing aggregation-based frameworks have several limitations. Some of these limitations include …

Classification Of Drainage Crossings On High-Resolution Digital Elevation Models: A Deep Learning Approach, Di Wu, Ruopu Li, Banafsheh Rekabdar, Claire Talbert, Michael Edidem, Guangxing Wang

Computer Science Faculty Publications and Presentations

High-Resolution Digital Elevation Models (HRDEMs) have been used to delineate fine-scale hydrographic features in landscapes with relatively level topography. However, artificial flow barriers associated with roads are known to cause incorrect modeled flowlines, because these barriers substantially increase the terrain elevation and often terminate flowlines. A common practice is to breach the elevation of roads near drainage crossing locations, which, however, are often unavailable. Thus, developing a reliable drainage crossing dataset is essential to improve the HRDEMs for hydrographic delineation. The purpose of this research is to develop deep learning models for classifying the images that contain the locations of …

Remote Neuronal Activity Drives Glioma Progression Through Sema4f, Emmet Huang-Hobbs, Yi-Ting Cheng, Yeunjung Ko, Estefania Luna-Figueroa, Brittney Lozzi, Kathryn R Taylor, Malcolm Mcdonald, Peihao He, Hsiao-Chi Chen, Yuhui Yang, Ehson Maleki, Zhung-Fu Lee, Sanjana Murali, Michael R Williamson, Dongjoo Choi, Rachel Curry, James Bayley, Junsung Woo, Ali Jalali, Michelle Monje, Jeffrey L Noebels, Akdes Serin Harmanci, Ganesh Rao, Benjamin Deneen

Faculty, Staff and Students Publications

The tumor microenvironment (TME) plays an essential role in malignancy and neurons have emerged as a key component of the TME that promotes tumorigenesis across a host of cancers1,2. Recent studies on glioblastoma (GBM) highlight bi-directional signaling between tumors and neurons that propagates a vicious cycle of proliferation, synaptic integration, and brain hyperactivity3-8; however, the identity of neuronal subtypes and tumor subpopulations driving this phenomenon are incompletely understood. Here we show that callosal projection neurons located in the hemisphere contralateral to primary GBM tumors promote progression and widespread infiltration. Using this platform …

Distinct Serum Immune Profiles Define The Spectrum Of Acute And Chronic Pancreatitis From The Multicenter Prospective Evaluation Of Chronic Pancreatitis For Epidemiologic And Translational Studies (Proceed) Study, Bomi Lee, Elaina K Jones, Murli Manohar, Liang Li, Dhiraj Yadav, Darwin L Conwell, Phil A Hart, Santhi Swaroop Vege, Evan L Fogel, Jose Serrano, Dana Andersen, Melena D Bellin, Mark D Topazian, Stephen K Van Den Eeden, Stephen J Pandol, Chris E Forsmark, William E Fisher, Walter G Park, Sohail Z Husain, Aida Habtezion, Consortium For The Study Of Chronic Pancreatitis, Diabetes, And Pancreatic Cancer (Cpdpc)

Faculty, Staff and Students Publications

BACKGROUND & AIMS: Pancreatitis is a disease continuum, starting with acute pancreatitis (AP) and progressing in some cases to recurrent acute pancreatitis (RAP) and chronic pancreatitis (CP). Currently, there are no approved therapies or early diagnostic or prognostic biomarkers for pancreatitis. The current study examined whether patient serum immune profiling could identify noninvasive biomarkers and provide mechanistic insight into the disease continuum of pancreatitis.

METHODS: Using Olink immunoassay, we assessed the protein levels of 92 immune markers in serum samples from participants enrolled in the Prospective Evaluation of Chronic Pancreatitis for Epidemiologic and Translational Studies (PROCEED) study of the Chronic …