Digital Commons Network^™

Neighborhood Deprivation And Residential Property Values Do Not Affect Weight Loss At 1 Or 3 Years After Bariatric Surgery, Silvia R Paz, Kristina H Lewis, Jialuo Liu, Darren D Moore, Sameer B Murali, Deborah R Young, Karen J Coleman

Faculty, Staff and Student Publications

OBJECTIVE: This study examined the association between individual- and neighborhood-level sociodemographic factors and surgical weight loss at 1 year (short term) and 3 years (long term).

METHODS: Data were obtained from the baseline survey of the BELONG (Bariatric Experience Long Term) prospective longitudinal cohort study. Individual-level self-reported data on sex, race and ethnicity, education, and household income were obtained by survey. Data from the 2010 US Census were used to calculate area Neighborhood Deprivation Index score and median value of owner-occupied housing units at the census tract level.

RESULTS: Patients (N = 1341) had a mean age of 43.4 (SD …

A Rare Case Of Survival After Traumatic Blunt Ventricular Rupture, Michael Wandling

Faculty, Staff and Student Publications

A male in his 40s presented to the trauma center via air ambulance after colliding with a cement wall at highway speeds. Cross-sectional imaging revealed a right ventricular pseudoaneurysm, confirmed by echocardiography. He was taken emergently to the operating room where he was found to have a pericardial laceration, hemopericardium and a right ventricular rupture, which was primarily repaired. Postoperatively, the patient was transferred to intensive care and after 34 days in the hospital was ultimately discharged home.

Hemoglobin Concentration Impacts Viscoelastic Hemostatic Assays In Icu Admitted Patients, Glenda Torres, Katherine Xu, Winni Yang, Azzurra Cottarelli, Ernest Moore, Angela Sauaia, Kirk Hansen, Angela Velazquez, Amelia Boehme, Athina Vrosgou, Shivani Ghoshal, Soojin Park, Sachin Agarwal, Jan Claassen, E Sander Connolly, Gebhard Wagener, Richard O Francis, Eldad Hod

Faculty, Staff and Student Publications

Objectives: Low hemoglobin concentration impairs clinical hemostasis across several diseases. It is unclear whether hemoglobin impacts laboratory functional coagulation assessments. We evaluated the relationship of hemoglobin concentration on viscoelastic hemostatic assays in intracerebral hemorrhage (ICH) and perioperative patients admitted to an ICU.

Design: Observational cohort study and separate in vitro laboratory study.

Setting: Multicenter tertiary referral ICUs.

Patients: Two acute ICH cohorts receiving distinct testing modalities: rotational thromboelastometry (ROTEM) and thromboelastography (TEG), and a third surgical ICU cohort receiving ROTEM were evaluated to assess the generalizability of findings across disease processes and testing platforms. A separate in vitro ROTEM laboratory …

J Mich Dent Assoc February 2023

The Journal of the Michigan Dental Association

Every month, The Journal of the Michigan Dental Association brings news, information, and features about Michigan dentistry to our state's oral health community and the MDA's 6,200+ members across the state. No publication reaches more Michigan dentists!

In this issue, the reader will find the following original content:

• A feature on the Interfaith Dental Refugee Program of Michigan
• Commentary on what makes membership matter and the ADA marketing shift on the value of membership.
• Complete details on the 2023 MDA Annual Session
• News you need, Editorial, and regular department articles on Dentistry and the Law, Staff Matters, and Advocacy.

A Computational Offloading Optimization Scheme Based On Deep Reinforcement Learning In Perceptual Network, Yongli Xing, Tao Ye, Sami Ullah, Muhammad Waqas, Hisham Alasmary, Zihui Liu

Research outputs 2022 to 2026

Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces …

Citation Impact And Reach Of The Ioc Sport And Exercise Medicine Consensus Statements, Lauren V. Fortington, Rebecca N. Handcock, Wayne Derman, Carolyn A. Emery, Kati Pasanen, Martin Schwellnus, Evert Verhagen, Caroline F. Finch

Research outputs 2022 to 2026

Background: Research evidence is commonly compiled into expert-agreed consensus statements or guidelines, with an increasing trend towards their publication in peer-reviewed journals. Prominent among these has been the publication of several International Olympic Committee (IOC) tatements to help inform sport and exercise medicine (SEM) practice. This study aimed to assess the citation impact and reach of the IOC statements published between 2003 and 2020. Method: Bibliometric analysis focused on identifying core publications (original statement and linked publications) and quantifying their academic citations (number and Field-Weighted Citation Impact (FWCI)) in journal articles up to February 2022. The analysis includes descriptive data …

First-Principles Calculations On The Electronic Structure, Mechanical, Vibrational, And Thermal Properties Of The Ca₃Bin Antiperovskite Alloy, Reem Maher Mohamad

Theses

Antiperovskites are the counterparts of perovskites. They have diverse properties that make them physically rich and technologically relevant. In the recent years, the alkaline earth based antiperovskites, especially the ones with small semiconductor bandgap, have captured tremendous attention due to their properties which make them efficient to be used in optoelectronic and thermoelectric applications. In this thesis, we are shading light on the alkaline antiperovskite ����₃������ due to its narrow bandgap. Thus, it can be considered as a promising candidate for thermoelectric and optoelectronic applications. Specifically, it is used to harvest waste thermal energy into electricity at low …

Heat Transfer In Mini-Channel Heat Exchanger With Smooth And Pin-Fins Surfaces, Mohammed Molham Daadoua

Theses

This research aims to investigate heat transfer of single-phase and two-phase fluids in mini-channel heat exchanger with smooth and pin-fins surfaces. The main goal of the study is to develop a high-performance heat exchanger for thermal management in various processing applications. This research is carried out using computational fluid dynamics (CFD) modeling and experimentations. The proposed method is to use enhanced structured pin-fins surfaces in a mini-channel heat exchanger to improve heat transfer. The pin-fin microchannel heat exchanger is investigated using a CFD method with single-phase fluid (water, air). The experimental investigations are conducted using single-phase and two-phase fluid involving …

Patient-Level And System-Level Determinants Of Stroke Fatality Across 16 Large Hospitals In Ghana And Nigeria: A Prospective Cohort Study, Fred S. Sarfo, Onoja M. Akpa, Bruce Ovbiagele, Albert Akpalu, Kolawole Wahab, Reginald Obiako, Morenikeji Komolafe, Lukman Owolabi, Godwin Ogbole, Philip Adebayo

Internal Medicine, East Africa

Background: Every minute, six indigenous Africans develop new strokes. Patient-level and system-level contributors to early stroke fatality in this region are yet to be delineated. We aimed to identify and quantify the contributions of patient-level and system-level determinants of inpatient stroke fatality across 16 hospitals in Ghana and Nigeria.

Methods: The Stroke Investigative Research and Educational Network (SIREN) is a multicentre study involving 16 sites in Ghana and Nigeria. Cases include adults (aged ≥18 years) with clinical and radiological evidence of an acute stroke. Data on stroke services and resources available at each study site were collected and analysed as …

Social Support And The Effects Of The Covid-19 Pandemic Among A Cohort Of People Living With Hiv (Plwh) In Western Kenya, Adel Mburia-Mwalili, Karla D. Wagner, Edith Kamaru Kwobah, Lukoye Atwoli, Maurice Aluda, Brianna Simmons, Jayne Lewis-Kulzer, Suzanne Goodrich, Kara Wools-Kaloustian, Jennifer L. Syvertsen

Internal Medicine, East Africa

As coronavirus disease (COVID-19) was declared a pandemic in 2020, countries around the world implemented various prevention strategies, such as banning of public and social gatherings, restriction in movement, etc. These efforts may have had a deleterious effect on already vulnerable populations, including people living with HIV (PLWH). PLWH were concerned about contracting COVID-19, the impact of COVID-19 on their social networks that provide social support, and the continued availability of antiretroviral medications during the pandemic. In addition, their mental health may have been exacerbated by the pandemic. The purpose of this study was to explore pandemic-related concerns among a …

The Death Of The Legal Subject, Katrina Geddes

Vanderbilt Journal of Entertainment & Technology Law

The law is often engaged in prediction. In the calculation of tort damages, for example, a judge will consider what the tort victim’s likely future earnings would have been, but for their particular injury. Similarly, when considering injunctive relief, a judge will assess whether the plaintiff is likely to suffer irreparable harm if a preliminary injunction is not granted. And for the purposes of a child custody evaluation, a judge will consider which parent will provide an environment that is in the best interests of the child.

Relative to other areas of law, criminal law is oversaturated with prediction. Almost …

Fa3: Fine-Grained Android Application Analysis, Yan Lin, Weng Onn Wong, Debin Gao

Research Collection School Of Computing and Information Systems

Understanding Android applications' behavior is essential to many security applications, e.g., malware analysis. Although many systems have been proposed to perform such dynamic analysis, they are limited by their applicable analysis environment (on device vs. emulator), transparency to subject apps, applicable runtime (Dalvik vs. ART), applicable system stack, or granularity. In this paper, we propose FA3 (Fine-Grained Android Application Analysis), a novel on-device, non-invasive, and fine-grained analysis platform by leveraging existing profiling mechanisms in the Android Runtime (ART) and kernel to inspect method invocations and control-flow transfers for both Java methods and third-party native libraries. FA3 embeds its tracing capability …

Lightweight And Non-Invasive User Authentication On Earables, Changshuo Hu, Xiao Ma, Dong Ma, Ting Dang

Research Collection School Of Computing and Information Systems

The widespread adoption of wireless earbuds has advanced the developments in earable-based sensing in various domains like entertainment, human-computer interaction, and health monitoring. Recently, researchers have shown an increased interest in user authentication using earables. Despite the successes witnessed in acoustic probing and speech based authentication systems, this paper proposed a lightweight and non-invasive ambient sound based user authentication scheme. It employs the difference between the in-ear and out-ear sounds to estimate the individual-specific occluded ear canal transfer function (OECTF). Specifically, the {out-ear, in-ear} scaling factors at different frequency bands are captured via linear regression and treated as the OECTF …

Effective Graph Kernels For Evolving Functional Brain Networks, Xinlei Wang, Jinyi Chen, Bing Tian Dai, Junchang Xin, Yu Gu, Ge Yu

Research Collection School Of Computing and Information Systems

The graph kernel of the functional brain network is an effective method in the field of neuropsychiatric disease diagnosis like Alzheimer's Disease (AD). The traditional static brain networks cannot reflect dynamic changes of brain activities, but evolving brain networks, which are a series of brain networks over time, are able to seize such dynamic changes. As far as we know, the graph kernel method is effective for calculating the differences among networks. Therefore, it has a great potential to understand the dynamic changes of evolving brain networks, which are a series of chronological differences. However, if the conventional graph kernel …

Web Apis: Features, Issues, And Expectations: A Large-Scale Empirical Study Of Web Apis From Two Publicly Accessible Registries Using Stack Overflow And A User Survey, Neng Zhang, Ying Zou, Xin Xia, David Lo, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

With the increasing adoption of services-oriented computing and cloud computing technologies, web APIs have become the fundamental building blocks for constructing software applications. Web APIs are developed and published on the internet. The functionality of web APIs can be used to facilitate the development of software applications. There are numerous studies on retrieving and recommending candidate web APIs based on user requirements from a large set of web APIs. However, there are very limited studies on the features of web APIs that make them more likely to be used and the issues of using web APIs in practice. Moreover, users' …

Generalization Bounds For Inductive Matrix Completion In Low-Noise Settings, Antoine Ledent, Rodrigo Alves, Yunwen Lei, Yann Guermeur, Marius Kloft

Research Collection School Of Computing and Information Systems

We study inductive matrix completion (matrix completion with side information) under an i.i.d. subgaussian noise assumption at a low noise regime, with uniform sampling of the entries. We obtain for the first time generalization bounds with the following three properties: (1) they scale like the standard deviation of the noise and in particular approach zero in the exact recovery case; (2) even in the presence of noise, they converge to zero when the sample size approaches infinity; and (3) for a fixed dimension of the side information, they only have a logarithmic dependence on the size of the matrix. Differently …

Mirror: Mining Implicit Relationships Via Structure-Enhanced Graph Convolutional Networks, Jiaying Liu, Feng Xia, Jing Ren, Bo Xu, Guansong Pang, Lianhua Chi

Research Collection School Of Computing and Information Systems

Data explosion in the information society drives people to develop more effective ways to extract meaningful information. Extracting semantic information and relational information has emerged as a key mining primitive in a wide variety of practical applications. Existing research on relation mining has primarily focused on explicit connections and ignored underlying information, e.g., the latent entity relations. Exploring such information (defined as implicit relationships in this article) provides an opportunity to reveal connotative knowledge and potential rules. In this article, we propose a novel research topic, i.e., how to identify implicit relationships across heterogeneous networks. Specially, we first give a …

Cross-Domain Graph Anomaly Detection Via Anomaly-Aware Contrastive Alignment, Qizhou Wang, Guansong Pang, Mahsa Salehi, Wray Buntine, Christopher Leckie

Research Collection School Of Computing and Information Systems

Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled anomalous and normal nodes. Although it presents a promising approach to address the notoriously high false positive issue in anomaly detection, little work has been done in this line of research. There are numerous domain adaptation methods in the literature, but it is difficult to adapt them for GAD due to the unknown distributions of the anomalies and the complex node relations embedded in graph data. To this end, we introduce a novel domain adaptation approach, …

Generalizing Math Word Problem Solvers Via Solution Diversification, Zhenwen Liang, Jipeng Zhang, Lei Wang, Yan Wang, Jie Shao, Xiangliang Zhang

Research Collection School Of Computing and Information Systems

Current math word problem (MWP) solvers are usually Seq2Seq models trained by the (one-problem; one-solution) pairs, each of which is made of a problem description and a solution showing reasoning flow to get the correct answer. However, one MWP problem naturally has multiple solution equations. The training of an MWP solver with (one-problem; one-solution) pairs excludes other correct solutions, and thus limits the generalizability of the MWP solver. One feasible solution to this limitation is to augment multiple solutions to a given problem. However, it is difficult to collect diverse and accurate augment solutions through human efforts. In this paper, …

Planning And Learning For Non-Markovian Negative Side Effects Using Finite State Controllers, Aishwarya Srivastava, Sandhya Saisubramanian, Praveen Paruchuri, Akshat Kumar, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Autonomous systems are often deployed in the open world where it is hard to obtain complete specifications of objectives and constraints. Operating based on an incomplete model can produce negative side effects (NSEs), which affect the safety and reliability of the system. We focus on mitigating NSEs in environments modeled as Markov decision processes (MDPs). First, we learn a model of NSEs using observed data that contains state-action trajectories and severity of associated NSEs. Unlike previous works that associate NSEs with state-action pairs, our framework associates NSEs with entire trajectories, which is more general and captures non-Markovian dependence on states …

Scalable And Globally Optimal Generalized L1 K-Center Clustering Via Constraint Generation In Mixed Integer Linear Programming, Aravinth Chembu, Scott Sanner, Hassan Khurran, Akshat Kumar

Research Collection School Of Computing and Information Systems

The k-center clustering algorithm, introduced over 35 years ago, is known to be robust to class imbalance prevalent in many clustering problems and has various applications such as data summarization, document clustering, and facility location determination. Unfortunately, existing k-center algorithms provide highly suboptimal solutions that can limit their practical application, reproducibility, and clustering quality. In this paper, we provide a novel scalable and globally optimal solution to a popular variant of the k-center problem known as generalized L_1 k-center clustering that uses L_1 distance and allows the selection of arbitrary vectors as cluster centers. We show that this clustering objective …

Learning To Count Isomorphisms With Graph Neural Networks, Xingtong Yu, Zemin Liu, Yuan Fang, Xinming Zhang

Research Collection School Of Computing and Information Systems

Subgraph isomorphism counting is an important problem on graphs, as many graph-based tasks exploit recurring subgraph patterns. Classical methods usually boil down to a backtracking framework that needs to navigate a huge search space with prohibitive computational costs. Some recent studies resort to graph neural networks (GNNs) to learn a low-dimensional representation for both the query and input graphs, in order to predict the number of subgraph isomorphisms on the input graph. However, typical GNNs employ a node-centric message passing scheme that receives and aggregates messages on nodes, which is inadequate in complex structure matching for isomorphism counting. Moreover, on …

On Generalized Degree Fairness In Graph Neural Networks, Zemin Liu, Trung Kien Nguyen, Yuan Fang

Research Collection School Of Computing and Information Systems

Conventional graph neural networks (GNNs) are often confronted with fairness issues that may stem from their input, including node attributes and neighbors surrounding a node. While several recent approaches have been proposed to eliminate the bias rooted in sensitive attributes, they ignore the other key input of GNNs, namely the neighbors of a node, which can introduce bias since GNNs hinge on neighborhood structures to generate node representations. In particular, the varying neighborhood structures across nodes, manifesting themselves in drastically different node degrees, give rise to the diverse behaviors of nodes and biased outcomes. In this paper, we first define …

Contrastive Learning Approach To Word-In-Context Task For Low-Resource Languages, Pei-Chi Lo, Yang-Yin Lee, Hsien-Hao Chen, Agus Trisnajaya Kwee, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Word in context (WiC) task aims to determine whether a target word’s occurrences in two sentences share the same sense. In this paper, we propose a Contrastive Learning WiC (CLWiC) framework to improve the learning of sentence/word representations and classification of target word senses in the sentence pair when performing WiC on lowresource languages. In representation learning, CLWiC trains a pre-trained language model’s ability to cope with lowresource languages using both unsupervised and supervised contrastive learning. The WiC classifier learning further finetunes the language model with WiC classification loss under two classifier architecture options, SGBERT and WiSBERT, which use single-encoder …

Learning Relation Prototype From Unlabeled Texts For Long-Tail Relation Extraction, Yixin Cao, Jun Kuang, Ming Gao, Aoying Zhou, Yonggang Wen, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by extracting entity relations from texts. However, it usually suffers from the long-tail issue. The training data mainly concentrates on a few types of relations, leading to the lack of sufficient annotations for the remaining types of relations. In this paper, we propose a general approach to learn relation prototypes from unlabeled texts, to facilitate the long-tail relation extraction by transferring knowledge from the relation types with sufficient training data. We learn relation prototypes as an implicit factor between entities, which reflects the meanings of relations as well …

Learning Comprehensive Global Features In Person Re-Identification: Ensuring Discriminativeness Of More Local Regions, Jiali Xia, Jianqiang Huang, Shibao Zheng, Qin Zhou, Bernt Schiele, Xian-Sheng Hua, Qianru Sun

Research Collection School Of Computing and Information Systems

Person re-identification (Re-ID) aims to retrieve person images from a large gallery given a query image of a person of interest. Global information and fine-grained local features are both essential for the representation. However, global embedding learned by naive classification model tends to be trapped in the most discriminative local region, leading to poor evaluation performance. To address the issue, we propose a novel baseline network that learns strong global feature termed as Comprehensive Global Embedding (CGE), ensuring more local regions of global feature maps to be discriminative. In this work, two key modules are proposed including Non-parameterized Local Classifier …

Online Hyperparameter Optimization For Class-Incremental Learning, Yaoyao Liu, Yingying Li, Bernt Schiele, Qianru Sun

Research Collection School Of Computing and Information Systems

Class-incremental learning (CIL) aims to train a classification model while the number of classes increases phase-by-phase. An inherent challenge of CIL is the stability-plasticity tradeoff, i.e., CIL models should keep stable to retain old knowledge and keep plastic to absorb new knowledge. However, none of the existing CIL models can achieve the optimal tradeoff in different data-receiving settings—where typically the training-from-half (TFH) setting needs more stability, but the training-from-scratch (TFS) needs more plasticity. To this end, we design an online learning method that can adaptively optimize the tradeoff without knowing the setting as a priori. Specifically, we first introduce the …

Real-Time Hierarchical Map Segmentation For Coordinating Multi-Robot Exploration, Tianze Luo, Zichen Chen, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Coordinating a team of autonomous agents to explore an environment can be done by partitioning the map of the environment into segments and allocating the segments as targets for the individual agents to visit. However, given an unknown environment, map segmentation must be conducted in a continuous and incremental manner. In this paper, we propose a novel real-time hierarchical map segmentation method for supporting multi-agent exploration of indoor environments, wherein clusters of regions of segments are formed hierarchically from randomly sampled points in the environment. Each cluster is then assigned with a cost-utility value based on the minimum cost possible …

Safe Delivery Of Critical Services In Areas With Volatile Security Situation Via A Stackelberg Game Approach, Tien Mai, Arunesh Sinha

Research Collection School Of Computing and Information Systems

Vaccine delivery in under-resourced locations with security risks is not just challenging but also life threatening. The COVID pandemic and the need to vaccinate added even more urgency to this issue. Motivated by this problem, we propose a general framework to set-up limited temporary (vaccination) centers that balance physical security and desired (vaccine) service coverage with limited resources. We set-up the problem as a Stackelberg game between the centers operator (defender) and an adversary, where the set of centers is not fixed a priori but is part of the decision output. This results in a mixed combinatorial and continuous optimization …

A Fair Incentive Scheme For Community Health Workers, Avinandan Bose, Tracey Li, Arunesh Sinha, Tien Mai

Research Collection School Of Computing and Information Systems

Community health workers (CHWs) play a crucial role in the last mile delivery of essential health services to under-served populations in low-income countries. Many non-governmental organizations (NGOs) provide training and support to enable CHWs to deliver health services to their communities, with no charge to the recipients of the services. This includes monetary compensation for the work that CHWs perform, which is broken down into a series of well-defined tasks. In this work, we partner with a NGO D-Tree International to design a fair monetary compensation scheme for tasks performed by CHWs in the semi-autonomous region of Zanzibar in Tanzania, …