Physical Sciences and Mathematics Commons^™

Engineering A Microbiosphere To Clean Up The Ocean – Inspiration From The Plastisphere, Khulood A. Alnahdi, Laila W. Alali, Mezna K. Suwaidan, M. Kalim Akhtar

All Works

Plastic is a ubiquitous material that has become an essential part of our lives. More than one hundred million tons of plastic has accumulated in the world’s oceans as a result of poor waste management. This plastic waste gradually fragments into smaller pieces known as microplastics and nanoplastics. These small plastic particles can cause significant damage to marine ecosystems, and negatively impact human health. According to a recent review of international patents, the majority of ocean-cleaning inventions are limited to microplastics larger than 20 μm. Furthermore, such technologies are ineffective for nanoplastics, which measure less than 1000 nm, or even …

Naming Venus: An Exploration Of Goddesses, Heroines, And Famous Women, Kavya Beheraj

Dissertations, Theses, and Capstone Projects

Humans have been observing and romanticizing Venus for more than 5,000 years. However, mapping its surface has nearly always been impossible, since the planet is shrouded in thick clouds. A breakthrough came just fifty years ago with the invention of radar imaging, leading to the discovery (and naming) of hundreds of new features in a relatively short length of time.

The rapid naming of Venus is a case study on the impact of planetary nomenclature — the process of naming features on other worlds. While the act of naming streamlines communication and humanizes alien landscapes, it is subject to bias, …

Nanoscale Imaging Of Electrocatalytic Nanomaterials By High-Resolution Scanning Electrochemical Microscopy, Xiang Wang

Dissertations, Theses, and Capstone Projects

Numerous insights of the structure–electrochemical activity relationship of nanocatalysts have been obtained by using macroscopic electrochemical measurements over the last several decades. However, signals measured by large electrodes are inevitably averaged out of many nanocatalysts, non-uniformed sizes, uneven morphologies, and multiple crystallographic facets. Over the last ten years, Scanning Electrochemical Microscopy (SECM) has advanced electrochemical measurements toward micro- to nanoscale level at a high spatial resolution. The advantages of using nanoelectrode in SECM include fast mass transfer of reactive species, dominated radial diffusion pattern, small double layer capacitance and small RC constant. In this thesis, high-resolution SECM is applied to …

2023 February - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Report

No abstract provided.

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 …

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 …

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 …

Layout Generation As Intermediate Action Sequence Prediction, Huiting Yang, Danqing Huang, Chin-Yew Lin, Shengfeng He

Research Collection School Of Computing and Information Systems

Layout generation plays a crucial role in graphic design intelligence. One important characteristic of the graphic layouts is that they usually follow certain design principles. For example, the principle of repetition emphasizes the reuse of similar visual elements throughout the design. To generate a layout, previous works mainly attempt at predicting the absolute value of bounding box for each element, where such target representation has hidden the information of higher-order design operations like repetition (e.g. copy the size of the previously generated element). In this paper, we introduce a novel action schema to encode these operations for better modeling the …

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, …

Mitigating Popularity Bias For Users And Items With Fairness-Centric Adaptive Recommendation, Zhongzhou Liu, Yuan Fang, Min Wu

Research Collection School Of Computing and Information Systems

Recommendation systems are popular in many domains. Researchers usually focus on the effectiveness of recommendation (e.g., precision) but neglect the popularity bias that may affect the fairness of the recommendation, which is also an important consideration that could influence the benefits of users and item providers. A few studies have been proposed to deal with the popularity bias, but they often face two limitations. Firstly, most studies only consider fairness for one side - either users or items, without achieving fairness jointly for both. Secondly, existing methods are not sufficiently tailored to each individual user or item to cope with …

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 …

Flexible Job-Shop Scheduling Via Graph Neural Network And Deep Reinforcement Learning, Wen Song, Xinyang Chen, Qiqiang Li, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Recently, deep reinforcement learning (DRL) has been applied to learn priority dispatching rules (PDRs) for solving complex scheduling problems. However, the existing works face challenges in dealing with flexibility, which allows an operation to be scheduled on one out of multiple machines and is often required in practice. Such one-to-many relationship brings additional complexity in both decision making and state representation. This article considers the well-known flexible job-shop scheduling problem and addresses these issues by proposing a novel DRL method to learn high-quality PDRs end to end. The operation selection and the machine assignment are combined as a composite decision. …

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 …

Using Virtual Simulations Of Future Extreme Weather Events To Communicate Climate Change Risk, Terry Van Gevelt, Brian G. Mcadoo, Jie Yang, Linlin Li, Fiona Williamson, Alex Scollay, Aileen Lam, Kwan Nok Chan, Adam D. Switzer

Research Collection College of Integrative Studies

Virtual simulations of future extreme weather events may prove an effective vehicle for climate change risk communication. To test this, we created a 3D virtual simulation of a future tropical cyclone amplified by climate change. Using an experimental framework, we isolated the effect of our simulation on risk perceptions and individual mitigation behaviour for a representative sample (n = 1507) of the general public in Hong Kong. We find that exposure to our simulation is systematically associated with a relatively small decrease in risk perceptions and individual mitigation behaviour. We suggest that this is likely due to climate change scepticism, …

The Synapse Issue 35

The Synapse: Intercollegiate science magazine

No abstract provided.

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 …

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 …

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 …

Bidding Graph Games With Partially-Observable Budgets, Guy Avni, Ismael Jecker, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Two-player zero-sum graph games are a central model, which proceeds as follows. A token is placed on a vertex of a graph, and the two players move it to produce an infinite play, which determines the winner or payoff of the game. Traditionally, the players alternate turns in moving the token. In bidding games, however, the players have budgets and in each turn, an auction (bidding) determines which player moves the token. So far, bidding games have only been studied as fullinformation games. In this work we initiate the study of partial-information bidding games: we study bidding games in which …

Learning Control Policies For Stochastic Systems With Reach-Avoid Guarantees, Dorde Zikelic, Mathias Lechner, A. Thomas Henzinger, Krishnendu Chatterjee

Research Collection School Of Computing and Information Systems

We study the problem of learning controllers for discrete-time non-linear stochastic dynamical systems with formal reach-avoid guarantees. This work presents the first method for providing formal reach-avoid guarantees, which combine and generalize stability and safety guarantees, with a tolerable probability threshold p ∈ [0,1] over the infinite time horizon in general Lipschitz continuous systems. Our method leverages advances in machine learning literature and it represents formal certificates as neural networks. In particular, we learn a certificate in the form of a reach-avoid supermartingale (RASM), a novel notion that we introduce in this work. Our RASMs provide reachability and avoidance guarantees …

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 …

International Advisory Proceedings On Climate Change, Benoit Mayer

Michigan Journal of International Law

Several island states are expected to be severely harmed by climate change and rising sea levels. In late 2021, several island states launched two legal initiatives aimed at requesting advisory opinions of international courts on the law applicable to climate change. In the hope of fostering more action to combat climate change, these states are asking international courts to clarify the obligations of states to cut greenhouse gas emissions and pay reparations for harm already caused.

This article provides the first comprehensive assessment of the feasibility and desirability of international advisory proceedings on climate change. It analyzes recent developments and …

Wayne E. Sabbe Arkansas Soil Fertility Studies 2022, Nathan A. Slaton, Mike Daniels

Arkansas Agricultural Experiment Station Research Series

Rapid technological changes in crop management and production require that the research efforts be presented in an expeditious manner. The contributions of soil fertility and fertilizers are major production factors in all Arkansas crops. The studies described within will allow producers to compare their practices with the university’s research efforts. Additionally, soil-test data and fertilizer sales are presented to allow comparisons among years, crops, and other areas within Arkansas.

Regulating Machine Learning: The Challenge Of Heterogeneity, Cary Coglianese

All Faculty Scholarship

Machine learning, or artificial intelligence, refers to a vast array of different algorithms that are being put to highly varied uses, including in transportation, medicine, social media, marketing, and many other settings. Not only do machine-learning algorithms vary widely across their types and uses, but they are evolving constantly. Even the same algorithm can perform quite differently over time as it is fed new data. Due to the staggering heterogeneity of these algorithms, multiple regulatory agencies will be needed to regulate the use of machine learning, each within their own discrete area of specialization. Even these specialized expert agencies, though, …

Long-Term Phosphorus Reduction And Phytoplankton Responses In An Urban Lake (Usa), Yuan Xiao Grund, Yangdong Pan, Mark Rosenkranz, Eugene Foster

Environmental Science and Management Faculty Publications and Presentations

Eutrophication is one of the primary factors causing harmful cyanobacteria blooms in freshwater lakes. This study investigated the long-term changes in water quality and summer phytoplankton assemblages in Oswego Lake, OR, USA, in relation to phosphorus reduction through hypolimnetic aeration and alum applications. Both water quality and phytoplankton assemblages were sampled biweekly during the summers from 2001 to 2013. The concentrations of total phosphorus, soluble reactive phosphorus, and total nitrogen decreased 66%, 93% and 31%, respectively, in response to the hypolimnetic aeration and alum treatments since 2005. Summer phytoplankton assemblages showed a 62% reduction of cyanobacteria biovolume and a …

Fluvial Flood Losses In The Contiguous United States Under Climate Change, M.M. Rashid, T. Wahl, G. Villarini, A. Sharma

Faculty Publications

Flooding is one of the most devastating natural disasters causing significant economic losses. One of the dominant drivers of flood losses is heavy precipitation, with other contributing factors such as built environments and socio-economic conditions superimposed to it. To better understand the risk profile associated with this hazard, we develop probabilistic models to quantify the future likelihood of fluvial flood-related property damage exceeding a critical threshold (i.e., high property damage) at the state level across the conterminous United States. The model is conditioned on indicators representing heavy precipitation amount and frequency derived from observed and downscaled precipitation. The likelihood of …

One-Loop Corrections To Dihadron Production In Dis At Small X, Filip Bergabo

Dissertations, Theses, and Capstone Projects

We calculate the one-loop corrections to dihadron production in Deep Inelastic Scattering (DIS) at small x using the Color Glass Condensate formalism. We show that all UV and soft singularities cancel while the collinear divergences are absorbed into quark and anti quark-hadron fragmentation functions. Rapidity divergences lead to JIMWLK evolution of dipoles and quadrupoles describing multiple-scatterings of the quark anti-quark dipole on the target proton/nucleus. The resulting cross section is finite and can be used for phenomenological studies of dihadron angular correlations at small x in a future Electron-Ion Collider (EIC).