Physical Sciences and Mathematics Commons^™

An Interdisciplinary Analysis Of Freshwater Wetlands With A Focus On Ecosystem Functions And Services, Michael Viteritto

Theses, Dissertations and Culminating Projects

Wetlands are one of the most productive ecosystems on Earth, though they only occupy a very small portion of the planet’s surface. They are highly sensitive ecosystems that have been undergoing an extended period of loss because of anthropogenic stress. Freshwater wetlands number among the most numerous on the planet, and in the United States they make up the overwhelming majority of wetland area. Geographically isolated wetlands (GIWs) are not directly connected to other surface water in a watershed. Freshwater wetlands provide a number of beneficial ecosystem services including climate control, erosion protection, nutrient removal and storage, flood control, food …

Onset Of Antarctic Glaciation At The Eocene-Oligocene Transition In The Weddell Sea And Its Impact On Ocean Circulation, Victoria Hojnacki

Theses, Dissertations and Culminating Projects

The Eocene – Oligocene Transition (EOT) (34 Ma) represents a greenhouse to icehouse climate transition that is marked by the appearance of permanent ice sheets in Antarctica. Studying this period of climate change allows us to understand how different Earth systems reacted under changing atmospheric CO₂ conditions. Though the timing of the development of the East Antarctic Ice Sheet is well supported, the development of the West Antarctic Ice Sheet is still up for debate. Ocean Drilling Program Site 696 is the only paleoarchive of the EOT in the Weddell Sea region and offers an opportunity to better constrain the …

Investigating The Applications Of Peps To The Measurement Of The Stabilities Of Staphylococcal Nuclease (Stw) And Human Acidic Fibroblast Growth Factor (Hfgf1), Motolani Omolade Matthew

Graduate Theses and Dissertations

This dissertation presents a detailed investigation into the applications of Protein Equilibrium Population Snapshot H/D Exchange Electrospray Ionization Mass Spectrometry (PEPS-H/D exchange or PEPS) method. This method is based on protein equilibrium unfolding, and its fundamental capability is to capture a "snapshot" of the protein's state, allowing us to monitor the ratio of the unfolded to folded populations at different chemical denaturant concentrations. First, to increase the accuracy of PEPS data analysis for assessing protein stability, the traditional linear extrapolation method (LEM) was replaced. Instead of using LEM, which calculates free energy change based on the peak ratio of unfolded …

Struvite Effects On Greenhouse Gas Emissions From Flood- And Furrow-Irrigated Rice In The Greenhouse, Chandler Arel

Graduate Theses and Dissertations

Over time, as the influences of climate-change and declining, mineable, global phosphorus (P) reserves begin to impact agricultural production systems, alternative fertilizer sources and management practices, such as struvite (MgNH₄PO₄ · 6H₂O) and furrow-irrigation, will be necessary to accommodate nutrient and water demands. The precipitation and use of struvite as a fertilizer-P source could reduce environmental damages caused by nutrient-rich wastewater streams, supplement domestic P supply, and improve the sustainability of flood-, and furrow-irrigated rice production by reducing greenhouse gas emissions. The study of struvite from both chemical and electrochemical precipitation processes is well documented, …

Tuning Electronic Transport And Magnetic Properties Of Layered Magnetic Materials, Md Rafique Un Nabi

Graduate Theses and Dissertations

Two-dimensional magnetic materials (2DMMs) have gained significant interest due to their potential for novel physical phenomena and device applications. Mn_2-xZn_xSb, with its tunable magnetic properties and stable tetragonal layered structure, offers a rich platform for exploring these phenomena. The introduction of Zn into the Mn₂Sb lattice efficiently tunes magnetic and electronic properties, making it a compelling candidate for high-performance spintronic devices.

With this motivation, we synthesized Mn_2-xZn_xSb (0 ≤ x ≤ 1) single crystals using a flux method. Mn2Sb exhibits ferrimagnetic order below 550 K with antiparallel spins aligned along …

Quantum Modeling & Experimental Studies Of Radical Chemistry, Justin Michael Barrett

Graduate Theses and Dissertations

This work describes two projects centered around radical chemical mechanisms. The first investigation generalized the utility of a C-N bond homolysis followed by a [1,3] – rearrangement synthetic strategy found possible in N-alkylated thiazole & benzothiazole species onto analogous systems. Computational investigation guided the inquiry on structurally analogous systems that would favor a radical, [1,3] – rearrangement. Herein, we describe the implementation of that mechanism to transform Breslow Intermediate-like substrates into tertiary alcohol and amide products under facile conditions. We confer on the proficiency of this strategy with 2-pyridinemethanol and the problematic implementation with 2-(aminomethyl)pyridine. The second investigation solved a …

Controls On The Development Of Hummocky Topography On Debris-Covered Glaciers, Ryan M. Strickland

Graduate Theses and Dissertations

Rock debris covers many glaciers in Earth’s highest mountains and the debris impacts glacier response to changes in climate. Interactions between debris, drainage, glacier dynamics, and climate produce contrasts in melt rates that cause debris-covered glaciers to develop complex, hummocky topography. Contrasts in melt rate produce thousands of hills–hummocks–and depressions. Ponds and ice cliffs often form in the depressions. Although ponds and ice cliffs occupy only a small fraction of the surface, they account for nearly half of glacier melt. Understanding the processes that control the development of hummocky topography is key to predicting the evolution of debris-covered glaciers. In …

Application Of Functionalized Organosilicas In Adsorption Of Nitrates, Stephen Amoako

Electronic Theses and Dissertations

This study addresses the critical environmental issue of elevated nitrate levels in water bodies, primarily due to excessive use of nitrogenous fertilizers and improper waste disposal. It focuses on reducing nitrate concentrations in polluted water to permissible levels through the effectiveness of hybrid materials in nitrate adsorption. We synthesized nine amino-functionalized adsorbents using grafting and sol-gel techniques. Batch adsorption tests confirmed the high nitrate adsorption capacities of these adsorbents, with sol-gel materials showing the highest efficiency due to their abundant amino group contents. Among these, the surfactant-free, sol-gel adsorbent was the most effective, combining ease of synthesis with cost-efficiency. Our …

Story Of Your Lazy Function's Life: A Bidirectional Demand Semantics For Mechanized Cost Analysis Of Lazy Programs, Li-Yao Xia, Laura Israel, Maite Kramarz, Nicholas Coltharp, Koen Claessen, Stephanie Weirich, Yao Li

Computer Science Faculty Publications and Presentations

Lazy evaluation is a powerful tool that enables better compositionality and potentially better performance in functional programming, but it is challenging to analyze its computation cost. Existing works either require manually annotating sharing, or rely on separation logic to reason about heaps of mutable cells. In this paper, we propose a bidirectional demand semantics that allows for extrinsic reasoning about the computation cost of lazy programs without relying on special program logics. To show the effectiveness of our approach, we apply the demand semantics to a variety of case studies including insertion sort, selection sort, Okasaki's banker's queue, and the …

Experimental Investigations Of Elemental And Isotopic Partitioning With Applications To Geochronology, Lunar Crust, And Meteorite Erg Chech 002, Yuanyuan Liang

Arts & Sciences Graduate Student Theses and Dissertations

Elemental partitioning and crystallization processes on the Earth, Moon, and the meteorite Erg Chech 002 parent body are explored in this thesis, highlighting different mineral assemblage crystallization histories in three distinct geologic environments: the Earth's crust, the Moon's mantle, and the Erg Chech 002 (EC 002) parent body. Multiple series of one-atmosphere zircon synthesizing experiments, using basaltic andesite, andesite, and rhyolite starting materials, have been conducted (Chapter 2) to study the U/Th partitioning behavior between zircon and melt. The results reveal that sector zoning, fractional crystallization, typical igneous oxygen fugacities, and melt composition, particularly the whole rock silica content and …

Interpretable Tensor Fusion, Saurabh Varshneya, Antoine Ledent, Philipp Liznerski, Andriy Balinskyy, Purvanshi Mehta, Waleed Mustafa, Marius Kloft

Research Collection School Of Computing and Information Systems

Conventional machine learning methods are predominantly designed to predict outcomes based on a single data type. However, practical applications may encompass data of diverse types, such as text, images, and audio. We introduce interpretable tensor fusion (InTense), a multimodal learning method training a neural network to simultaneously learn multiple data representations and their interpretable fusion. InTense can separately capture both linear combinations and multiplicative interactions of the data types, thereby disentangling higher-order interactions from the individual effects of each modality. InTense provides interpretability out of the box by assigning relevance scores to modalities and their associations, respectively. The approach is …

Solving Long-Run Average Reward Robust Mdps Via Stochastic Games, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Mehrdad Karrabi, Petr Novotný, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Markov decision processes (MDPs) provide a standard framework for sequential decision making under uncertainty. However, MDPs do not take uncertainty in transition probabilities into account. Robust Markov decision processes (RMDPs) address this shortcoming of MDPs by assigning to each transition an uncertainty set rather than a single probability value. In this work, we consider polytopic RMDPs in which all uncertainty sets are polytopes and study the problem of solving long-run average reward polytopic RMDPs. We present a novel perspective on this problem and show that it can be reduced to solving long-run average reward turn-based stochastic games with finite state …

Task Scheduling Strategy For 3dpcp Considering Multidynamic Information Perturbation In Green Scene, Jianjia He, Jian Wu, Keng Siau

Research Collection School Of Computing and Information Systems

The 3D printing cloud platform (3DPCP) plays a pivotal role in breaking down the information silos between supply and demand, effectively reducing waste through information integration and intelligent production. However, due to the complexity of 3DPCP scheduling in green scenes and the multidynamic information perturbations, unveils problems in traditional task scheduling methods in 3DPCP. These issues manifest as incomplete considerations, subpar green performance, and weak adaptability to dynamic changes. There is an urgent need to design practical methods to realize the multidynamic information perturbations in green scenes within 3DPCP. Therefore, this article first defines the 3DPCP task scheduling problem for …

Peep With A Mirror: Breaking The Integrity Of Android App Sandboxing Via Unprivileged Cache Side Channel, Yan Lin, Joshua Wong, Xiang Li, Haoyu Ma, Debin Gao

Research Collection School Of Computing and Information Systems

Application sandboxing is a well-established security principle employed in the Android platform to safeguard sensitive information. However, hardware resources, specifically the CPU caches, are beyond the protection of this software-based mechanism, leaving room for potential side-channel attacks. Existing attacks against this particular weakness of app sandboxing mainly target shared components among apps, hence can only observe system-level program dynamics (such as UI tracing). In this work, we advance cache side-channel attacks by demonstrating the viability of non-intrusive and fine-grained probing across different app sandboxes, which have the potential to uncover app-specific and private program behaviors, thereby highlighting the importance of …

Optimization Of Customer Service And Driver Dispatch Areas For On-Demand Food Delivery, Jingfeng Yang, Hoong Chuin Lau, Hai Wang

Research Collection School Of Computing and Information Systems

With the rapid development and popularization of mobile and wireless communication technologies, on-demand food delivery (OFD) platforms have been able to connect restaurants, customers, and drivers in real time, drastically changing dining and food delivery services. Motivated by the critical need for supply and demand management in the on-demand food delivery market, we focus on the optimization of customer service area and driver dispatch area for on-demand food delivery services. Specifically, for each restaurant, the platform needs to decide the (1) customer service area (CSA), i.e., the surrounding area within which customers can see the restaurant’s information and order food …

Anopay: Anonymous Payment For Vehicle Parking With Updatable Credential, Yang Yang, Wenyi Xue, Yonghua Zhan, Minming Huang, Yingjiu Li, Robert H. Deng

Research Collection School Of Computing and Information Systems

Many existing anonymous parking payment schemes lack high efficiency and flexibility. For instance, the calculation and communication costs involved in payment may linearly increase with the payment amount. In this paper, we propose an anonymous payment system (dubbed AnoPay) for vehicle parking, which leverages updatable attribute-based anonymous credentials and efficient zero-knowledge proof (ZKP) to achieve user anonymity and constant overhead for parking fee payment. To further improve the efficiency, we design a secure parking fee aggregation protocol based on linear homomorphic encryption to aggregate parking transactions, where the amount of each parking transaction is hidden and the privacy of the …

Is Aggregation The Only Choice? Federated Learning Via Layer-Wise Model Recombination, Ming Hu, Zhihao Yue, Xiaofei Xie, Cheng Chen Chen

Research Collection School Of Computing and Information Systems

Although Federated Learning (FL) enables global model training Xiaofei Xie [email protected] Singapore Management University Singapore, Singapore Xian Wei [email protected] East China Normal University Shanghai, China Mingsong Chen∗ [email protected] East China Normal University Shanghai, China • Computing methodologies → Distributed artificial intelligence. across clients without compromising their raw data, due to the unevenly distributed data among clients, existing Federated Averaging (FedAvg)-based methods suffer from the problem of low inference performance. Specifically, different data distributions among clients lead to various optimization directions of local models. Aggregating local models usually results in a low-generalized global model, which performs worse on most of the …

Sibo : A Simple Booster For Parameter-Efficient Fine-Tuning, Zhihao Wen, Jie Zhang, Yuan Fang

Research Collection School Of Computing and Information Systems

Fine-tuning all parameters of large language models (LLMs) necessitates substantial computational power and extended time. Latest advancements in parameter-efficient fine-tuning (PEFT) techniques, such as Adapter tuning and LoRA, allow for adjustments to only a minor fraction of the parameters of these LLMs. Concurrently, it has been noted that the issue of over-smoothing diminishes the effectiveness of these Transformer-based LLMs, resulting in suboptimal performances in downstream tasks. In this paper, we present SIBO, which is a SImple BOoster to enhance PEFT, by injecting an initial residual. SIBO is straightforward and readily extensible to a range of state-of-the-art PEFT techniques to alleviate …

Tackling Stackelberg Network Interdiction Against A Boundedly Rational Adversary, Tien Mai, Avinandan Bose, Arunesh Sinha, Thanh Nguyen, Ayushman Kumar Singh

Research Collection School Of Computing and Information Systems

This work studies Stackelberg network interdiction games --- an important class of games in which a defender first allocates (randomized) defense resources to a set of critical nodes on a graph while an adversary chooses its path to attack these nodes accordingly. We consider a boundedly rational adversary in which the adversary's response model is based on a dynamic form of classic logit-based (quantal response) discrete choice models. The resulting optimization is non-convex and additionally, involves complex terms that sum over exponentially many paths. We tackle these computational challenges by presenting new efficient algorithms with solution guarantees. First, we present …

Planning Like Human : A Dual-Process Framework For Dialogue Planning, Tao He, Lizi Liao, Yixin Cao, Yuanxing Liu, Ming Liu, Zerui Chen, Bing Qin

Research Collection School Of Computing and Information Systems

In proactive dialogue, the challenge lies not just in generating responses but in steering conversations toward predetermined goals, a task where Large Language Models (LLMs) typically struggle due to their reactive nature. Traditional approaches to enhance dialogue planning in LLMs, ranging from elaborate prompt engineering to the integration of policy networks, either face efficiency issues or deliver suboptimal performance. Inspired by the dual-process theory in psychology, which identifies two distinct modes of thinking—intuitive (fast) and analytical (slow), we propose the Dual-Process Dialogue Planning (DPDP) framework. DPDP embodies this theory through two complementary planning systems: an instinctive policy model for familiar …

Analyzing Temporal Complex Events With Large Language Models? A Benchmark Towards Temporal, Long Context Understanding, Zhihan Zhang, Yixin Cao, Chenchen Ye, Ma. Yunshan, Lizi Liao, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

The digital landscape is rapidly evolving with an ever-increasing volume of online news, emphasizing the need for swift and precise analysis of complex events.We refer to the complex events composed of many news articles over an extended period as Temporal Complex Event (TCE). This paper proposes a novel approach using Large Language Models (LLMs) to systematically extract and analyze the event chain within TCE, characterized by their key points and timestamps. We establish a benchmark, named TCELongBench, to evaluate the proficiency of LLMs in handling temporal dynamics and understanding extensive text. This benchmark encompasses three distinct tasks - reading comprehension, …

Synergizing Large Language Models And Pre-Trained Smaller Models For Conversational Intent Discovery, Jinggui Liang, Lizi Liao, Hao Fei, Jing Jiang

Research Collection School Of Computing and Information Systems

In Conversational Intent Discovery (CID), Small Language Models (SLMs) struggle with overfitting to familiar intents and fail to label newly discovered ones. This issue stems from their limited grasp of semantic nuances and their intrinsically discriminative framework. Therefore, we propose Synergizing Large Language Models (LLMs) with pre-trained SLMs for CID (SynCID). It harnesses the profound semantic comprehension of LLMs alongside the operational agility of SLMs. By utilizing LLMs to refine both utterances and existing intent labels, SynCID significantly enhances the semantic depth, subsequently realigning these enriched descriptors within the SLMs’ feature space to correct cluster distortion and promote robust learning …

A Survey On Neural Question Generation : Methods, Applications, And Prospects, Shasha Guo, Lizi Liao, Cuiping Li, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

In this survey, we present a detailed examination of the advancements in Neural Question Generation (NQG), a field leveraging neural network techniques to generate relevant questions from diverse inputs like knowledge bases, texts, and images. The survey begins with an overview of NQG’s background, encompassing the task’s problem formulation, prevalent benchmark datasets, established evaluation metrics, and notable applications. It then methodically classifies NQG approaches into three predominant categories: structured NQG, which utilizes organized data sources, unstructured NQG, focusing on more loosely structured inputs like texts or visual content, and hybrid NQG, drawing on diverse input modalities. This classification is followed …

Reachability-Aware Fair Influence Maximization, Wenyue Ma, Maximilian K. Egger, Andreas Pavlogiannis, Yuchen Li, Panagiotis Karras

Research Collection School Of Computing and Information Systems

How can we ensure that an information dissemination campaign reaches every corner of society and also achieves high overall reach? The problem of maximizing the spread of influence over a social network has commonly been considered with an aggregate objective. Less attention has been paid to achieving equality of opportunity, reducing information barriers, and ensuring that everyone in the network has a fair chance to be reached. To that end, the fairness objective aims to maximize the minimum probability of reaching an individual. To address this inapproximable problem, past research has proposed heuristics, which, however, perform less well when the …

Firm-Level Ai Ethical Awareness: Measurement And Effects, Yan Ma, Nan Hu

Research Collection School Of Computing and Information Systems

With rapid integration of AI technologies into business operation, ethical issues associated with AI development, implementation, and deployment, has become increasingly conspicuous. An effective measure of firm-level AI Ethical Awareness (FAIEA) is crucial for both academics and practitioners to assess FAIEA efficiently. In this study, based on qualitative information in earning conference call, we construct and validate a firm specific measure of FAIEA. Upon establishing a reliable measure, we explore the determinants and consequence of FAIEA. We find Corporate Social Responsibility (CSR) engagement and the presence of a Chief Information Officer (CIO) as key determinants of higher FAIEA, suggesting the …

Unifying Global-Local Representations In Salient Object Detection With Transformers, Sucheng Ren, Nanxuan Zhao, Qiang Wen, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

The fully convolutional network (FCN) has dominated salient object detection for a long period. However, the locality of CNN requires the model deep enough to have a global receptive field and such a deep model always leads to the loss of local details. In this paper, we introduce a new attention-based encoder, vision transformer, into salient object detection to ensure the globalization of the representations from shallow to deep layers. With the global view in very shallow layers, the transformer encoder preserves more local representations to recover the spatial details in final saliency maps. Besides, as each layer can capture …

Speaker Verification In Agent-Generated Conversations, Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

The recent success of large language models (LLMs) has attracted widespread interest to develop role-playing conversational agents personalized to the characteristics and styles of different speakers to enhance their abilities to perform both general and special purpose dialogue tasks. However, the ability to personalize the generated utterances to speakers, whether conducted by human or LLM, has not been well studied. To bridge this gap, our study introduces a novel evaluation challenge: speaker verification in agent-generated conversations, which aimed to verify whether two sets of utterances originate from the same speaker. To this end, we assemble a large dataset collection encompassing …

Towards Gradient-Based Time-Series Explanations Through A Spatiotemporal Attention Network, Min Hun Lee

Research Collection School Of Computing and Information Systems

In this paper, we explore the feasibility of using a transformer-based, spatiotemporal attention network (STAN) for gradient-based time-series explanations. First, we trained the STAN model for video classifications using the global and local views of data and weakly supervised labels on time-series data (i.e. the type of an activity). We then leveraged a gradient-based XAI technique (e.g. saliency map) to identify salient frames of time-series data. According to the experiments using the datasets of four medically relevant activities, the STAN model demonstrated its potential to identify important frames of videos.

Value-Based Subgoal Discovery And Path Planning For Reaching Long-Horizon Goals, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan, Chai Quek

Research Collection School Of Computing and Information Systems

Learning to reach long-horizon goals in spatial traversal tasks is a significant challenge for autonomous agents. Recent subgoal graph-based planning methods address this challenge by decomposing a goal into a sequence of shorter-horizon subgoals. These methods, however, use arbitrary heuristics for sampling or discovering subgoals, which may not conform to the cumulative reward distribution. Moreover, they are prone to learning erroneous connections (edges) between subgoals, especially those lying across obstacles. To address these issues, this article proposes a novel subgoal graph-based planning method called learning subgoal graph using value-based subgoal discovery and automatic pruning (LSGVP). The proposed method uses a …

Larp: Language Audio Relational Pre‑Training For Cold‑Start Playlist Continuation, Rebecca Salganik, Xiaohao Liu, Yunshan Ma, Jian Kang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

As online music consumption increasingly shifts towards playlist-based listening, the task of playlist continuation, in which an algorithm suggests songs to extend a playlist in a personalized and musically cohesive manner, has become vital to the success of music streaming services. Currently, many existing playlist continuation approaches rely on collaborative filtering methods to perform their recommendations. However, such methods will struggle to recommend songs that lack interaction data, an issue known as the cold-start problem. Current approaches to this challenge design complex mechanisms for extracting relational signals from sparse collaborative signals and integrating them into content representations. However, these approaches …