Physical Sciences and Mathematics Commons^™

Permission, Prohibition, And Dynamism, John Thrasher

Philosophy Faculty Books and Book Chapters

Modern, open societies are characterized by growth and dynamism. To many environmentalists, however, it is this very commitment to growth and change that put modern societies at odds with the values of environmental protection. Many argue that the uncertainty that comes from growth endangers the environment, with the implication being that we should adopt general prohibitions rules like the “Precautionary Principle,” which prohibits economic activity unless it can be proven to have no substantial environmental impact. I argue that this conclusion is largely mistaken. Dynamism is so important and in opposition to general prohibition rules that we should avoid them …

Alignment-Enriched Tuning For Patch-Level Pre-Trained Document Image Models, Lei Wang, Jiabang He, Xing Xu, Ning Liu, Hui Liu

Research Collection School Of Computing and Information Systems

Alignment between image and text has shown promising im provements on patch-level pre-trained document image mod els. However, investigating more effective or finer-grained alignment techniques during pre-training requires a large amount of computation cost and time. Thus, a question natu rally arises: Could we fine-tune the pre-trained models adap tive to downstream tasks with alignment objectives and achieve comparable or better performance? In this paper, we pro pose a new model architecture with alignment-enriched tuning (dubbed AETNet) upon pre-trained document image models, to adapt downstream tasks with the joint task-specific super vised and alignment-aware contrastive objective. Specifically, weintroduce an extra …

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 …

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 …

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. …

Pose- And Attribute-Consistent Person Image Synthesis, Cheng Xu, Zejun Chen, Jiajie Mai, Xuemiao Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

PersonImageSynthesisaimsattransferringtheappearanceofthesourcepersonimageintoatargetpose. Existingmethods cannot handle largeposevariations and therefore suffer fromtwocritical problems: (1)synthesisdistortionduetotheentanglementofposeandappearanceinformationamongdifferentbody componentsand(2)failureinpreservingoriginalsemantics(e.g.,thesameoutfit).Inthisarticle,weexplicitly addressthesetwoproblemsbyproposingaPose-andAttribute-consistentPersonImageSynthesisNetwork (PAC-GAN).Toreduceposeandappearancematchingambiguity,weproposeacomponent-wisetransferring modelconsistingoftwostages.Theformerstagefocusesonlyonsynthesizingtargetposes,whilethelatter renderstargetappearancesbyexplicitlytransferringtheappearanceinformationfromthesourceimageto thetargetimageinacomponent-wisemanner. Inthisway,source-targetmatchingambiguityiseliminated duetothecomponent-wisedisentanglementofposeandappearancesynthesis.Second,tomaintainattribute consistency,werepresenttheinputimageasanattributevectorandimposeahigh-levelsemanticconstraint usingthisvectortoregularizethetargetsynthesis.ExtensiveexperimentalresultsontheDeepFashiondataset demonstratethesuperiorityofourmethodoverthestateoftheart,especiallyformaintainingposeandattributeconsistenciesunderlargeposevariations.

Constrained Reinforcement Learning In Hard Exploration Problems, Pankayaraj Pathmanathan, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

One approach to guaranteeing safety in Reinforcement Learning is through cost constraints that are imposed on trajectories. Recent works in constrained RL have developed methods that ensure constraints can be enforced even at learning time while maximizing the overall value of the policy. Unfortunately, as demonstrated in our experimental results, such approaches do not perform well on complex multi-level tasks, with longer episode lengths or sparse rewards. To that end, wepropose a scalable hierarchical approach for constrained RL problems that employs backward cost value functions in the context of task hierarchy and a novel intrinsic reward function in lower levels …

Future Aware Pricing And Matching For Sustainable On-Demand Ride Pooling, Xianjie Zhang, Pradeep Varakantham, Hao Jiang

Research Collection School Of Computing and Information Systems

The popularity of on-demand ride pooling is owing to the benefits offered to customers (lower prices), taxi drivers (higher revenue), environment (lower carbon footprint due to fewer vehicles) and aggregation companies like Uber (higher revenue). To achieve these benefits, two key interlinked challenges have to be solved effectively: (a) pricing – setting prices to customer requests for taxis; and (b) matching – assignment of customers (that accepted the prices) to taxis/cars. Traditionally, both these challenges have been studied individually and using myopic approaches (considering only current requests), without considering the impact of current matching on addressing future requests. In this …

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 …

An Enhanced Cloud-Native Deep Learning Pipeline For The Classification Of Network Traffic, Ahmed Sobhy Elkenawy

Theses and Dissertations

In a rapidly changing world, the way of solving real-world problems has changed to leverage the power of the advancements in multiple fields. Cloud-native computing approaches can be utilized with deep learning techniques to provide solutions in several important areas. For instance, with the emergence of the pandemic, much dependence on modern technologies came out as a replacement for face-to-face interaction. Deep learning can reach a high level of accuracy, which makes it very effective in the support of modern services and technologies. However, there are some challenging issues because deep learning requires many large-scale experiments, which demand a lot …

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 …

Carla+: An Evolution Of The Carla Simulator For Complex Environment Using A Probabilistic Graphical Model, Sumbal Malik, Manzoor Ahmed Khan, Aadam, Hesham El-Sayed, Farkhund Iqbal, Jalal Khan, Obaid Ullah

All Works

In an urban and uncontrolled environment, the presence of mixed traffic of autonomous vehicles, classical vehicles, vulnerable road users, e.g., pedestrians, and unprecedented dynamic events makes it challenging for the classical autonomous vehicle to navigate the traffic safely. Therefore, the realization of collaborative autonomous driving has the potential to improve road safety and traffic efficiency. However, an obvious challenge in this regard is how to define, model, and simulate the environment that captures the dynamics of a complex and urban environment. Therefore, in this research, we first define the dynamics of the envisioned environment, where we capture the dynamics relevant …

Gamesmanship In Modern Discovery Tech, Neel Guha, Peter Henderson, Diego A. Zambrano

Faculty Scholarship

This chapter explores the potential for gamesmanship in technology-assisted discovery. Attorneys have long embraced gamesmanship strategies in analog discovery, producing reams of irrelevant documents, delaying depositions, or interpreting requests in a hyper-technical manner. The new question, however, is whether machine learning technologies can transform gaming strategies. By now it is well known that technologies have reinvented the practice of civil litigation and, specifically, the extensive search for relevant documents in complex cases. Many sophisticated litigants use machine learning algorithms – under the umbrella of “Technology Assisted Review” (TAR) – to simplify the identification and production of relevant documents in discovery. …

E Pluribus Unum: Prospective Acceptability Benchmarking From The Contouring Collaborative For Consensus In Radiation Oncology Crowdsourced Initiative For Multiobserver Segmentation, Diana Lin, Kareem A Wahid, Benjamin E Nelms, Renjie He, Mohammed A Naser, Michael V Sherer, Abdallah S R Mohamed, Michael Cislo, James D Murphy, Clifton D Fuller, Erin F Gillespie

Faculty, Staff and Student Publications

Purpose: Contouring Collaborative for Consensus in Radiation Oncology (C3RO) is a crowdsourced challenge engaging radiation oncologists across various expertise levels in segmentation. An obstacle to artificial intelligence (AI) development is the paucity of multiexpert datasets; consequently, we sought to characterize whether aggregate segmentations generated from multiple nonexperts could meet or exceed recognized expert agreement.

Approach: Participants who contoured ≥1 region of interest (ROI) for the breast, sarcoma, head and neck (H&N), gynecologic (GYN), or gastrointestinal (GI) cases were identified as a nonexpert or recognized expert. Cohort-specific ROIs were combined into single simultaneous truth and performance level estimation (STAPLE) consensus segmentations. …

Optimized Peptide Nanomaterials As Delivery Vehicles For Hydrophobic Metal-Based Anticancer Agents, Yaron Marciano

Dissertations, Theses, and Capstone Projects

Enzyme-responsive materials have been well explored, particularly as therapeutic and diagnostic agents. In this thesis we demonstrate that anionic self-assembling peptides can be utilized as delivery vehicles for metal-based hydrophobic payloads. The tunability of the system is highlighted as well as the increase in cytotoxicity and selectivity in vitro. The rapid degradation of peptides in cell media may lead to the formation of new peptide-drug bioconjugates with increased activity and selectivity. The physiological stability of these peptide delivery vehicles has been optimized by capping the N-terminus with an acetyl group. This simple backbone modification was shown to not prevent self-assembly, …

Design And Applications Of Fmoc-Histidine And Aspartic Acid Tripeptides As Proton Conductive Materials, Shejla S. Pollozi

Dissertations, Theses, and Capstone Projects

The increasing demand for clean and sustainable energy has driven forward rapid improvements in polymer-electrolyte fuel cells and the use of ion-conducting polymers (ionomers) as proton exchange membranes and catalyst binders. While perfluorinated electrolytes have been commercially used as conductive membranes for nearly four decades, a new class of sustainable, conductive materials has emerged, inspired by the efficiency of charge transport processes in biological systems. These molecules, knows as peptide amphiphiles (PAs), have gained popularity as building blocks for the bottom-up fabrication of nanomaterials through supramolecular self-assembly. Supramolecular nanostructures, often in the form of hydrogels, are held together via non-covalent …

Unlocking Potential: The School-To-Prison Pipeline For Students With Disabilities, Navena F. Chaitoo

Dissertations, Theses, and Capstone Projects

This research uses quasi-experimental, matched sampling to examine the school-to-prison pipeline for students with disabilities using data from the National Longitudinal Study of Adolescent to Adult Health. This study presents novel insights into an at-risk group that has faced disproportionate rates of school discipline and incarceration. The study finds school suspension to be associated with future involvement in the criminal legal system and lower educational attainment. Disability was not found to mediate the relationship between suspension and future involvement in the criminal legal system or the relationship between suspension and academic outcomes. However, disability was found to be a statistically …

Dynamics In Designed Elastins And Enzymes, Jonathan M. Preston

Dissertations, Theses, and Capstone Projects

In this dissertation I present experimental results on the structure and dynamics of elastin, an elastomeric extracellular component in vertebrates that, in mammals, provides structure and elasticity to the circulatory system, lungs, skin and joints. These experiments, using a set of designed elastin like proteins with properties analogous to the natural protein, show that elastin is highly disordered and connect this disorder to its mechanism of assembly and elasticity. In addition, I also present preliminary work on imparting flexibility into a set of computationally designed metalloenzymes to broaden the range of catalytic activities they perform.

Analyzing Relationships With Machine Learning, Oscar Ko

Dissertations, Theses, and Capstone Projects

Procedurally, this project aims to take a dataset, analyze it, and offer insights to the audience in an easy-to-digest format. Conceptually, this project will seek to explore questions like: “Do couples that meet through online dating or dating apps have higher or lower quality relationships?”, “Can any features in this dataset help predict how a subject would rate their relationship quality?”, and “What other insights can I derive from using machine learning for exploratory analysis?” The intended audience for this project is anyone interested in romantic relationships or machine learning.

The dataset is from a Stanford University survey, “How Couples …

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 …

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, …

Revealing The Three-Dimensional Magnetic Texture With Machine Learning Models, Shihua Zhao

Dissertations, Theses, and Capstone Projects

Revealing three-dimensional (3D) magnetic textures with vector field electron tomography (VFET) is essential in studying novel magnetic materials with topologically protected spin textures potentially being used in the next-generation semiconductor industry. In this dissertation, we use machine learning (ML) models to reconstruct 3D magnetic textures from electron holography (EH) data.

We can feed the EH data, a series of two-dimensional (2D) phasemaps, into a neural network (NN) architecture directly or feed the EH data into a conventional VFET and then feed the reconstructed results into a NN. Thus, perceptive NN, either a simple convolutional neural network (CNN) or Unet architecture, …

A Quantum Approach To Language Modeling, Constantijn Van Der Poel

Dissertations, Theses, and Capstone Projects

This dissertation consists of six chapters. . . Chapter 1: We introduce language modeling, outline the software used for this thesis, and discuss related work. Chapter 2: We will unpack the transition from classical to quantum probabilities, as well as motivate their use in building a model to understand language-like datasets. Chapter 3: We motivate the Motzkin dataset, the models we will be investigating, as well as the necessary algorithms to do calculations with them. Chapter 4: We investigate our models’ sensitivity to various hyperparameters. Chapter 5: We compare the performance and robustness of the models. Chapter 6: We conclude …

Engineered Plga Nanofibers For Drug Delivery, Andrew Mancuso

Dissertations, Theses, and Capstone Projects

Poly-lactic-co-glycolic acid (PLGA) polymers are rapidly gaining momentum as a platform for next-generation drug delivery systems due to their ease and low cost of synthesis, favorable biocompatibility and biodegradability, and lack of toxicity. In particular, the application of drug-loaded PLGA nanofibers directly to a target tissue may represent a promising alternative to traditional routes of drug administration (e.g., oral, injectable) as they offer the potential to greatly improve bioavailability, increase efficacy, and reduce off-target toxicity. Furthermore, the use of such a system may potentially allow for greater flexibility in clinical drug development, by enabling the use of compounds which have …

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).

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 …

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 …

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 …