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Cross-Domain Graph Anomaly Detection Via Anomaly-Aware Contrastive Alignment, Qizhou Wang, Guansong Pang, Mahsa Salehi, Wray Buntine, Christopher Leckie Feb 2023

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, …

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Generalizing Math Word Problem Solvers Via Solution Diversification, Zhenwen Liang, Jipeng Zhang, Lei Wang, Yan Wang, Jie Shao, Xiangliang Zhang Feb 2023

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, …

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Planning And Learning For Non-Markovian Negative Side Effects Using Finite State Controllers, Aishwarya Srivastava, Sandhya Saisubramanian, Praveen Paruchuri, Akshat Kumar, Shlomo Zilberstein Feb 2023

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 …

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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 Feb 2023

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 …

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Learning To Count Isomorphisms With Graph Neural Networks, Xingtong Yu, Zemin Liu, Yuan Fang, Xinming Zhang Feb 2023

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 …

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On Generalized Degree Fairness In Graph Neural Networks, Zemin Liu, Trung Kien Nguyen, Yuan Fang Feb 2023

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 …

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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 Feb 2023

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 …

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Learning Relation Prototype From Unlabeled Texts For Long-Tail Relation Extraction, Yixin Cao, Jun Kuang, Ming Gao, Aoying Zhou, Yonggang Wen, Tat-Seng Chua Feb 2023

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 …

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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 Feb 2023

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 …

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Online Hyperparameter Optimization For Class-Incremental Learning, Yaoyao Liu, Yingying Li, Bernt Schiele, Qianru Sun Feb 2023

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 …

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Real-Time Hierarchical Map Segmentation For Coordinating Multi-Robot Exploration, Tianze Luo, Zichen Chen, Budhitama Subagdja, Ah-Hwee Tan Feb 2023

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 …

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Safe Delivery Of Critical Services In Areas With Volatile Security Situation Via A Stackelberg Game Approach, Tien Mai, Arunesh Sinha Feb 2023

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 …

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A Fair Incentive Scheme For Community Health Workers, Avinandan Bose, Tracey Li, Arunesh Sinha, Tien Mai Feb 2023

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, …

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Scalable And Globally Optimal Generalized L1 K-Center Clustering Via Constraint Generation In Mixed Integer Linear Programming, Aravinth Chembu, Scott Sanner, Hassan Khurram, Akshat Kumar Feb 2023

Scalable And Globally Optimal Generalized L1 K-Center Clustering Via Constraint Generation In Mixed Integer Linear Programming, Aravinth Chembu, Scott Sanner, Hassan Khurram, 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 L1 k-center clustering that uses L1 distance and allows the selection of arbitrary vectors as cluster centers. We show that this clustering objective …

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Human-Centered Ai For Software Engineering: Requirements, Reflection, And Road Ahead, David Lo Feb 2023

Human-Centered Ai For Software Engineering: Requirements, Reflection, And Road Ahead, David Lo

Research Collection School Of Computing and Information Systems

Since its inception in the 2000s, AI for Software Engineering (AI4SE) has grown rapidly. AI in its different forms, e.g., data mining, information retrieval, machine learning, natural language processing, etc., has been demonstrated to be able to produce good results for automating many tasks, including specification mining, bug and vulnerability discovery, bug localization, duplicate bug report identification, failure detection, program repair, technical question answering, code search, and many more. AI4SE has much potential to improve software engineers’ productivity and software quality. Due to its potential, it is currently one of the most popular research areas in the software engineering field.To …

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An Empirical Study Of Package Management Issues Via Stack Overflow, Syful Islam, Raula Kula, Christoph Treude, Bodin Chinthanet, Takashi Ishio, Kenichi Matsumoto Feb 2023

An Empirical Study Of Package Management Issues Via Stack Overflow, Syful Islam, Raula Kula, Christoph Treude, Bodin Chinthanet, Takashi Ishio, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

The package manager (PM) is crucial to most technology stacks, acting as a broker to ensure that a verified dependency package is correctly installed, configured, or removed from an application. Diversity in technology stacks has led to dozens of PMs with various features. While our recent study indicates that package management features of PM are related to end-user experiences, it is unclear what those issues are and what information is required to resolve them. In this paper, we have investigated PM issues faced by end-users through an empirical study of content on Stack Overflow (SO). We carried out a qualitative …

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Quantization-Aware Interval Bound Propagation For Training Certifiably Robust Quantized Neural Networks, Mathias Lechner, Dorde Zikelic, Krishnendu Chatterjee, A. Thomas Henzinger, Daniela Rus Feb 2023

Quantization-Aware Interval Bound Propagation For Training Certifiably Robust Quantized Neural Networks, Mathias Lechner, Dorde Zikelic, Krishnendu Chatterjee, A. Thomas Henzinger, Daniela Rus

Research Collection School Of Computing and Information Systems

We study the problem of training and certifying adversarially robust quantized neural networks (QNNs). Quantization is a technique for making neural networks more efficient by running them using low-bit integer arithmetic and is therefore commonly adopted in industry. Recent work has shown that floating-point neural networks that have been verified to be robust can become vulnerable to adversarial attacks after quantization, and certification of the quantized representation is necessary to guarantee robustness. In this work, we present quantization-aware interval bound propagation (QA-IBP), a novel method for training robust QNNs. Inspired by advances in robust learning of non-quantized networks, our training …

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Oria Stage Ii Expansion: Keep River Post-Development Aquatic Fauna And Targeted Sawfish Survey - 2022, Indopacific Environmental, Department Of Primary Industries And Regional Development, Western Australia Feb 2023

Oria Stage Ii Expansion: Keep River Post-Development Aquatic Fauna And Targeted Sawfish Survey - 2022, Indopacific Environmental, Department Of Primary Industries And Regional Development, Western Australia

Irrigated Agricultural Development in the North

This report presents the results of the 2022 Keep River Aquatic Fauna and Targeted Sawfish Survey which represents the final post-development survey required as per the project conditions of the Goomig Irrigation Development. Unlike the 2021 survey, which was affected by pandemic related travel restrictions that prohibited some sites from being surveyed, all regular monitoring sites were able to be sampled in 2022. Field work was undertaken September 2022 and sampling and analyses undertaken during the current study aligned with standardised methodologies set forth in the Ord River Irrigation Area – Weaber Plain Development Project Aquatic Fauna Management Plan.

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Building Resilience In Graduate Nurses Through Online Virtual Simulation, Robert Grant Harmon Feb 2023

Building Resilience In Graduate Nurses Through Online Virtual Simulation, Robert Grant Harmon

Electronic Theses and Dissertations

Resilience has become a popular topic in nursing research. Nurses experience enormous stress throughout their training process that extends into their first employment as they transition into practice. Simulation training is a standard for training nurses and for continuing professional education within academia. Most simulation training for nurses is focused on building critical thinking skills. While critical thinking is an essential quality equipping nurses to meet professional demands, resilience is a crucial quality to overcome the stress of both training and practice. Resilience is believed to play an essential role in the retention of new graduate nurses and preventing burnout …

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The Perceptions Of Faculty And Instructional Designers Regarding The Impact Of Professional Development To Teach Online Courses, Scott Mitchell January Feb 2023

The Perceptions Of Faculty And Instructional Designers Regarding The Impact Of Professional Development To Teach Online Courses, Scott Mitchell January

Electronic Theses and Dissertations

Leadership at a mid-sized 4-year university in the western region of Texas asserted a problem with the low amount of faculty training impacting online courses’ creation and quality. The purpose of this qualitative single case study was to investigate the elements needed to design quality online courses, determine how faculty and instructional designers perceived professional development provided by the university to develop online courses, and find out how faculty and instructional designers perceived professional development impacted faculty skills as an online instructor. Three research questions were created which generated two interview protocols. The following research questions were used and were …

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Examining Leadership Experiences And Practices Of African American Women In Higher Education Settings To Overcome Barriers, Tanya Stubbs White Feb 2023

Examining Leadership Experiences And Practices Of African American Women In Higher Education Settings To Overcome Barriers, Tanya Stubbs White

Electronic Theses and Dissertations

Abstract: This qualitative case study described the steps 11 African American women have practiced in growing and continuing their path to notable success in higher education leadership. No one size will fit all, but the methods used by African American women may guide other African American or culturally diverse women on how to transcend into and ascertain the well-deserved leadership roles in higher education administration or faculty careers. This study provided narratives of the women leaders to explain their journey to a leadership role. The purpose of the study was to describe the path, barriers, and supports that African American …

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Smartphone Use And Daily Cognitive Failures: A Critical Examination Using A Daily Diary Approach With Objective Smartphone Measures, Andree Hartanto, Kristine Y. X. Lee, Yi Jing Chua, Frosch Y. X. Quek, Nadyannam M. Majeed Feb 2023

Smartphone Use And Daily Cognitive Failures: A Critical Examination Using A Daily Diary Approach With Objective Smartphone Measures, Andree Hartanto, Kristine Y. X. Lee, Yi Jing Chua, Frosch Y. X. Quek, Nadyannam M. Majeed

Research Collection School of Social Sciences

While smartphones have brought many benefits and conveniences to users, there is continuing debate regarding their potential negative consequences on everyday cognition such as daily cognitive failures. A few cross-sectional studies have found positive associations between smartphone use and cognitive failures. However, several research gaps remain, such as the use of cross-sectional designs, confounds related to stable individual differences, the lack of validity in self-report measures of smartphone use, memory biases in retrospective self-reports, and the lack of differentiation between smartphone checking and smartphone screen time. To simultaneously address the aforementioned shortcomings, the current study examined the within-person associations between …

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Executive Function Deficits And Borderline Personality Disorder Symptomatology In A Nonclinical Adult Sample: A Latent Variable Analysis, Keisha D. Veerapandian, Gabriel X. D. Tan, Nadyanna M. Majeed, Andree Hartanto Feb 2023

Executive Function Deficits And Borderline Personality Disorder Symptomatology In A Nonclinical Adult Sample: A Latent Variable Analysis, Keisha D. Veerapandian, Gabriel X. D. Tan, Nadyanna M. Majeed, Andree Hartanto

Research Collection School of Social Sciences

While borderline personality disorder (BPD) symptomatology has been studied extensively in clinical populations, the mechanisms underlying its manifestation in nonclinical populations remain largely understudied. One aspect of BPD symptomatology in nonclinical populations that has not been well studied is cognitive mechanisms, especially in relation to executive functions. To explore the cognitive mechanisms underlying BPD symptomatology in nonclinical populations, we analysed a large-scale dataset of 233 young adults that were administered with nine executive function tasks and BPD symptomatology assessments. Our structural equation modelling did not find any significant relations between latent factors of executive functions and the severity of BPD …

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A Narrative Review Of Screen Time And Wellbeing Among Adolescents Before And During The Covid-19 Pandemic: Implications For The Future, Verity Yu Qing Lua, Terence B. K. Chua, Michael Y. H. Chia Feb 2023

A Narrative Review Of Screen Time And Wellbeing Among Adolescents Before And During The Covid-19 Pandemic: Implications For The Future, Verity Yu Qing Lua, Terence B. K. Chua, Michael Y. H. Chia

Research Collection School of Social Sciences

The COVID-19 pandemic has disrupted the lives of many. In particular, during the height of the pandemic, many experienced lockdowns, which in turn increased screen time drastically. While the pandemic has been declared an endemic and most activities have been reinstated, there appears to still be elevated screen time among adolescents due to poor habits formed during the pandemic lockdowns. This paper explores the factors by which screen time affects well-being among adolescents and how the pandemic may have influenced some of these factors. For example, beyond having greater screen time, many adolescents have also reduced their physical activities and …

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The Study Of Followers In Leadership Research: A Systematic And Critical Review, Burak Oc, Kraivin Chintakananda, Michael R. Bashshur, David V. Day Feb 2023

The Study Of Followers In Leadership Research: A Systematic And Critical Review, Burak Oc, Kraivin Chintakananda, Michael R. Bashshur, David V. Day

Research Collection Lee Kong Chian School Of Business

Despite the significant amount of existing research examining the relationship of follower-related factors with leadership outcomes, there is no systematic, critical review that integrates and helps leadership scholars make sense of this rapidly growing body of research. To address this gap in the literature, we first briefly discuss the leading perspectives explaining the role of followers in leadership. Next, we identify and discuss the most frequently studied theoretical narratives explaining the relationship between follower-related predictors and leadership outcomes. Because theoretical arguments generally make causal claims, we identify and examine how methodological concerns including power analysis, multicollinearity, and endogeneity might prevent …

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Deep Feature Meta-Learners Ensemble Models For Covid-19 Ct Scan Classification, Jibin B. Thomas, K. V. Shihabudheen, Sheik Mohammed Sulthan, Adel Al-Jumaily Feb 2023

Deep Feature Meta-Learners Ensemble Models For Covid-19 Ct Scan Classification, Jibin B. Thomas, K. V. Shihabudheen, Sheik Mohammed Sulthan, Adel Al-Jumaily

Research outputs 2022 to 2026

The infectious nature of the COVID-19 virus demands rapid detection to quarantine the infected to isolate the spread or provide the necessary treatment if required. Analysis of COVID-19-infected chest Computed Tomography Scans (CT scans) have been shown to be successful in detecting the disease, making them essential in radiology assessment and screening of infected patients. Single-model Deep CNN models have been used to extract complex information pertaining to the CT scan images, allowing for in-depth analysis and thereby aiding in the diagnosis of the infection by automatically classifying the chest CT scan images as infected or non-infected. The feature maps …

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Using Nanomaterials As Excellent Immobilisation Layer For Biosensor Design, Azeez Olayiwola Idris, Seyi Philemon Akanji, Benjamin O. Orimolade, Foluke Omobola Grace Olorundare, Shohreh Azizi, Bhekie Mamba, Malik Maaza Feb 2023

Using Nanomaterials As Excellent Immobilisation Layer For Biosensor Design, Azeez Olayiwola Idris, Seyi Philemon Akanji, Benjamin O. Orimolade, Foluke Omobola Grace Olorundare, Shohreh Azizi, Bhekie Mamba, Malik Maaza

Research outputs 2022 to 2026

The endless development in nanotechnology has introduced new vitality in device fabrication including biosensor design for biomedical applications. With outstanding features like suitable biocompatibility, good electrical and thermal conductivity, wide surface area and catalytic activity, nanomaterials have been considered excellent and promising immobilisation candidates for the development of high-impact biosensors after they emerged. Owing to these reasons, the present review deals with the efficient use of nanomaterials as immobilisation candidates for biosensor fabrication. These include the implementation of carbon nanomaterials—graphene and its derivatives, carbon nanotubes, carbon nanoparticles, carbon nanodots—and MXenes, likewise their synergistic impact when merged with metal oxide nanomaterials. …

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Transeuntis Mundi, A Nomadic Artistic Practice, Cândida Luiza Borges Da Silva Feb 2023

Transeuntis Mundi, A Nomadic Artistic Practice, Cândida Luiza Borges Da Silva

School of Law, Humanities and Social Sciences Theses

In this practice-led Ph.D. research, I investigate how an artistic practice can respond to the migration phenomena performed by human beings across the planet over millennia ¬– what I refer to as the millennial global human journey. Based on the idea of mobility, I chose to frame this research in the articulation of concepts deriving from the prefix trans: transculture, transhumance and transmediality. This research contributes to studies in art composition by developing the processes and concept of transmedial composition, mainly contributing to the field of New Media Art. This investigation resulted in the work Transeuntis Mundi (TM) Project – …

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Beyond Varices: Complications Of Cirrhotic Portal Hypertension In Pediatrics, Benjamin L Shneider Feb 2023

Beyond Varices: Complications Of Cirrhotic Portal Hypertension In Pediatrics, Benjamin L Shneider

Faculty, Staff and Students Publications

Complications of cirrhotic portal hypertension (PHTN) in children are broad and include clinical manifestations ranging from variceal hemorrhage, hepatic encephalopathy (HE), ascites, spontaneous bacterial peritonitis (SBP), and hepatorenal syndrome (HRS) to less common conditions such as hepatopulmonary syndrome, portopulmonary hypertension, and cirrhotic cardiomyopathy. The approaches to the diagnosis and management of these complications have become standard of practice in adults with cirrhosis with many guidance statements available. However, there is limited literature on the diagnosis and management of these complications of PHTN in children with much of the current guidance available focused on variceal hemorrhage. The aim of this review …

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Current Challenges And Future Direction In Surveillance For Hepatocellular Carcinoma In Patients With Nonalcoholic Fatty Liver Disease Feb 2023

Current Challenges And Future Direction In Surveillance For Hepatocellular Carcinoma In Patients With Nonalcoholic Fatty Liver Disease

Faculty, Staff and Students Publications

The burden for hepatocellular carcinoma (HCC) attributed to nonalcoholic fatty liver disease (NAFLD) continues to grow in parallel with rising global trends in obesity. The risk of HCC is elevated among patients with NAFLD-related cirrhosis to a level that justifies surveillance based on cost-effectiveness argument. The quality of current evidence for HCC surveillance in all patients with chronic liver disease is poor, and even lower in those with NAFLD. For a lack of more precise risk-stratification tools, current approaches to defining a target population in noncirrhotic NAFLD are limited to noninvasive tests for liver fibrosis, as a proxy for liver-related …

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