Physical Sciences and Mathematics Commons^™

Modeling And Dynamics Of Capillary Bridges, Moyosore Odunsi

Dissertations, Theses, and Capstone Projects

Capillary bridges are ubiquitous in nature and have important implications in processes like inkjet printing. They are an important area of study not only because of their industrial applications but because many of the questions in capillary bridge research are applicable to other systems. For example, they exhibit pinning and unpinning patterns that are similar to those in sliding droplets. The rules underlying this pinning are essential to predicting liquid shapes and understanding how contact lines move across surfaces. Many previous studies have focused on axisymmetric capillary bridges and neglected to model the tangential forces that arise due to asymmetry. …

Parabolic And Non-Parabolic Surfaces With Small Or Large End Spaces Via Fenchel-Nielsen Parameters, Michael Antony Pandazis

Dissertations, Theses, and Capstone Projects

We consider conditions on the Fenchel-Nielsen parameters of a Riemann surface X that determine whether or not a surface X is parabolic. Fix a geodesic pants decomposition of a surface and call the boundary geodesics in the decomposition cuffs. For a zero or half-twist flute surface, we prove that parabolicity is equivalent to the surface having a covering group of the first kind. Using that result, we give necessary and sufficient conditions on the Fenchel-Nielsen parameters of a half-twist flute surface X with increasing cuff lengths such that X is parabolic. As an application, we determine whether or not each …

Communication Challenges In Underwater Wireless Networks: Mac Protocols And Software Solutions, Dmitrii Dugaev

Dissertations, Theses, and Capstone Projects

Underwater wireless networks (UWNs) represent a diverse and intriguing research domain, encompassing a wide array of scientific and industrial applications. This dissertation delves into the communication challenges at the Medium Access Control (MAC) layer within UWNs, stemming from the distinctive signal propagation conditions and the harshness of the deployment environment. The manuscript provides comprehensive coverage of key aspects of UWNs, including potential applications, communication protocols, methodologies employed in such networks, and existing software solutions that facilitate simulation, emulation, and real testbed scenarios for underwater research endeavors. Furthermore, this research introduces innovative software and communication solutions designed to facilitate the seamless …

Higher Diffeology Theory, Emilio Minichiello

Dissertations, Theses, and Capstone Projects

Finite dimensional smooth manifolds have been studied for hundreds of years, and a massive theory has been built around them. However, modern mathematicians and physicists are commonly dealing with objects outside the purview of classical differential geometry, such as orbifolds and loop spaces. Diffeology is a new framework for dealing with such generalized smooth spaces. This theory (whose development started in earnest in the 1980s) has started to catch on amongst the wider mathematical community, thanks to its simplicity and power, but it is not the only approach to dealing with generalized smooth spaces. Higher topos theory is another such …

Effect Of Magnetic Draping On Satellite Galaxies In Clusters, Vanessa Brown

Dissertations, Theses, and Capstone Projects

Galaxy evolution has been observed to be influenced by environment. Satellite galaxies orbiting within clusters can experience changes in morphology and composition through various mechanisms such as ram-pressure stripping (RPS), which removes a galaxy’s interstellar medium as it passes through the cluster via direct interaction with the hot intracluster medium gas. An open question is whether intracluster magnetic fields affect galaxy evolution, for example by forming a magnetic layer around infalling galaxies (called magnetic draping) and mitigating gas removal by RPS. Using the code GADGET-3, we compare global properties and mass distributions within identical cluster simulations run with and without …

Development And Application Of Magnus Expansion Based Propagators For Problems In Spectroscopy And Quantum Dynamics, Taner M. Ture

Dissertations, Theses, and Capstone Projects

Stable and accurate numerical propagators of time-evolution equations in quantum mechanics are required to capture correct dynamical behavior, especially in the long time limit. Magnus expansion (ME) provides a general way to expand the real time propagator of a time dependent Hamiltonian within the exponential such that the unitarity is satisfied at any order. Integrators are developed by truncating the ME and using explicit integration of Lagrange interpolation formulas for the time dependent Hamiltonian within each time interval. The derived approximations are studied in a numerical test and compared to other available expressions. The sixth order expression is applied to …

Sigma_N-Correct Forcing Axioms, Benjamin P. Goodman

Dissertations, Theses, and Capstone Projects

I introduce a new family of axioms extending ZFC set theory, the Sigma_n-correct forcing axioms. These assert roughly that whenever a forcing name a' can be forced by a poset in some forcing class Gamma to have some Sigma_n property phi which is provably preserved by all further forcing in Gamma, then a' reflects to some small name such that there is already in V a filter which interprets that small name so that phi holds. Sigma_1-correct forcing axioms turn out to be equivalent to classical forcing axioms, while Sigma_2-correct forcing axioms for Sigma_2-definable forcing classes are consistent relative to …

Speeding Up Coded Distributed Machine Learning, Xian Su

Dissertations, Theses, and Capstone Projects

The advancement of artificial intelligence has facilitated the generation of vast datasets, whose size often exceeds tens of terabytes. Meanwhile, machine learning has become the most important technique for big data analytics, and sophisticated models with thousands or even millions of parameters are designed to leverage big data. However, processing big data and training large models necessitate significant storage and computational resources, which has been a bottleneck in the development of artificial intelligence. Distributed computing, such as MapReduce, has been proposed to surmount the computational bottlenecks associated with single-machine analysis.

Although distributed computing harnesses the collective power of numerous less …

Explicit Composition Identities For Higher Composition Laws In The Quadratic Case, Ajith A. Nair

Dissertations, Theses, and Capstone Projects

The theory of Gauss composition of integer binary quadratic forms provides a very useful way to compute the structure of ideal class groups in quadratic number fields. In addition to that, Gauss composition is also important in the problem of representations of integers by binary quadratic forms. In 2001, Bhargava discovered a new approach to Gauss composition which uses 2x2x2 integer cubes, and he proved a composition law for such cubes. Furthermore, from the higher composition law on cubes, he derived four new higher composition laws on the following spaces - 1) binary cubic forms, 2) pairs of binary quadratic …

Unconventional Computing With Photonic Oscillator Networks, Mostafa Honari Latifpour

Dissertations, Theses, and Capstone Projects

The ever-increasing demand for data processing and the challenges in scaling traditional computing architectures are driving intensive research into alternative computing paradigms. Optical computing has garnered renewed attention since the 2010s, driven by its potential to accelerate specialized computational tasks such as combinatorial optimization and neural networks.

Coherent light sources including lasers and parametric oscillators have been around for decades and become indispensable tools in modern technology, but these photonic oscillators are also nonlinear dynamical systems that can exhibit emergent, complex phenomena, especially when coupled in arrays. These nonlinear optical systems have recently been shown to be capable of doing …

Aspects Of Parity Breaking In Classical And Quantum Fluids, Dylan J. Reynolds

Dissertations, Theses, and Capstone Projects

Parity-breaking is ubiquitous across many scales of physics, from the rotation of galaxies at the largest of scales, to the cyclotron orbits of electrons at the microscopic scale. In describing the collective dynamics of many particle systems, parity breaking effects typically originate from some form of chirality, such as angular momentum, at the level of the constituent particles. External forces can also induce chiral motion, with the primary examples being the Lorentz and Coriolis forces.

The effects of parity breaking are perhaps most strikingly seen in active matter, systems of complex particles that tend to convert energy into some directed …

Using Gaussian Processes To Measure M-Dwarf Rotation Periods From Ground-Based Light Curves, Ryan J. Lebron

Dissertations, Theses, and Capstone Projects

Stellar rotation is a readily observable characteristic that plays a crucial role in the generation and activity of magnetic fields through a process known as a magnetic dynamo. For low mass main sequence stars, they exhibit fully convective interiors, giving rise to a distinct dynamo mechanism compared to solar-type stars. Examining the rotational speeds of these stars can offer valuable insights into the workings of these mechanisms. To measure these rotation periods, we developed a pipeline to analyze 192 archival light curves of low mass stars observed by the Zwicky Transient Facility (ZTF) by utilizing a combination of Lomb-Scargle and …

Me And Mathematics: “Doing What You’Re Talking About”: In Dialogue With My Family, Eden Morris

Dissertations, Theses, and Capstone Projects

This paper is a philosophically oriented accompaniment to my audio project (accessible through the following link: https://cuny.manifoldapp.org/projects/me-and-mathematics). Working together, the paper and audio collages form a call to action and a resource. My primary finding is the importance of doing what you’re talking about or exploring and implementing your ideas experientially. Doing what you’re talking about is important for effective teaching/learning and feeling in line with oneself. This working concept came to my attention during my research conversation with my oldest living relative, and then, again, with my youngest (non-baby) relative. This doing what you’re talking about is a way …

Illustris-Tng Simulated Central Black Mass(Mbh) And Galaxy Properties Correlations With A Machine Learning Approach, Imani L. Dindy

Dissertations, Theses, and Capstone Projects

Observationaly it is well established that the masses of central black holes are tightly correlated with galaxy properties, most notably the bulge’s velocity dispersion. Cosmolog- ical hydrodynamical simulations can capture most of these correlations, but it is yet not understood why this occurs. To gain greater insight into central black hole growth we use machine learning algorithms to study the relationship between central black hole mass(MBH) and other galaxy properties at z=0 in the TNG simulations. We find that the central black hole mass can be accurately predicted with just a few galaxy properties only if the central black hole …

The Efficacy Of Using Machine Learning Techniques For Identifying And Classifying “Fake News”, Muhammad Islam

Dissertations, Theses, and Capstone Projects

In today's digital world, detecting fake news has emerged as a critical challenge, one that has significant effects on democracy and public discourse at large both regionally and globally. This research studies how diversity of news sources in training datasets affects how well machine learning models can classify fake vs true news. I used the Linear Support Vector Classification (LinearSVC) to create and compare two classification models: one was trained on a dataset that only had real news from a singular source, Reuters (Dataset 1), and the other was trained on a dataset that contained real news from Reuters, The …

Using A Novel Chain Of Chemical, Crystallographic, And Isotopic Analytical Techniques To Examine The Formation Histories Of Features In Chondritic Meteorites And Orbicular Granites, Samuel P. Alpert

Dissertations, Theses, and Capstone Projects

Understanding the chemistry, crystallography, and isotopic variability of minerals allows us to place significant constraints on the formation history of their host rocks. These constraints provide insight into everything from the distribution of water in the solar system to the onset of plate tectonics. Electron beam instruments and ion probes are important tools used by modern geologists to obtain crystallographic, chemical, and isotopic data. Here I present a new workflow, using these instruments, combining wavelength dispersive spectrometry (WDS), backscattered electron (BSE) imaging, machine learning algorithms, electron backscatter diffraction (EBSD), and secondary ion mass spectroscopy (SIMS), to place quantitative constraints on …

Modeling Hydroxide In Classical Proton Hopping Simulations And Amyloid Beta Membrane Pores, Ankita Dutta

Dissertations, Theses, and Capstone Projects

H3O+ and OH- ions display anomalously large diffusion coefficients, a property that is often attributed to their ability to perform structural diffusion which allow these ions to move through aqueous solutions by sequential proton transfers, also known as ‘Grotthuss hopping’. A rearrangement of the local solvation structure is known to be the prerequisite for proton transfer events via these ions. Studying this phenomenon using classical molecular dynamics simulations that do not allow bond rearrangements poses significant obstacles. Here we use a Monte Carlo augmented classical Molecular Dynamics method called the MOBHY algorithm which allows instantaneous changes in protonation states in …

Settingsorder Article Reprints Open Accesseditor’S Choicearticle Exploring The Impact Of Spray Process Parameters On Graphite Coatings: Morphology, Thickness, And Tribological Properties, Adedoyin Abe, Josue A. Goss, Min Zou

Mechanical Engineering Faculty Publications and Presentations

This study explores, through a full factorial experimental design, the effects of graphite concentration and spray flow rate on the morphology, thickness, and tribological performance of graphite coatings for potential tribological applications. Coatings were applied to rough substrates using varying concentrations and flow rates, followed by analysis of their morphological characteristics, roughness, thickness, coefficient of friction (COF), and wear behavior. The results revealed distinct differences in the coating morphology based on flow rate, with low-flow-rate coatings exhibiting a porous structure and higher roughness, while high-flow-rate coatings displayed denser structures with lower roughness. A COF as low as 0.09 was achieved, …

Development Of Platinum(Iv)-Gold(I)-Based Anticancer Agents, Javier E. Lopez-Hernandez

Dissertations, Theses, and Capstone Projects

Heterometallic compounds have emerged as prospective multitarget anticancer drugs. These species may display improved efficacy and physicochemical properties compared to the individual metallic fragments, while helping combat drug resistance by exerting effects on multiple pathways. In this thesis we describe the synthesis of new bi- and tri-metallic compounds based on platinum(IV) compounds containing FDA-approved platinum agents, and gold(I) derivatives with known anticancer properties. We demonstrate their toxicity and selectivity against a small panel of renal, bladder, ovarian, and breast cancer cell lines, as well as their synergistic effects. We have selected a trinuclear PtAu₂compound containing the Pt(IV) core …

Antibiotic Bone Cement Accelerates Diabetic Foot Wound Healing-Elucidating The Role Of Rock1 Protein Expression

Faculty, Staff and Student Publications

No abstract provided.

High Rates Of Erosion On A Wave-Exposed Fringing Coral Reef, Damian P. Thomson, Shannon Dee, Christopher Doropoulos, Melanie Orr, Shaun K. Wilson, Andrew S. Hoey

Research outputs 2022 to 2026

Erosion is a key process in shaping the physical structure of coral reefs, yet due to erosion being semi-cryptic and difficult to quantify, information remains limited. Here, we investigate erosional processes along Ningaloo Reef, an extensive fringing coral reef in Western Australia. We employed both direct and indirect methods to measure erosion in wave-exposed reef slopes and protected lagoonal habitats. Direct measurements of erosion on coral blocks were among the highest found globally, with total erosion of 3.07 kg m−2 yr−1 (4% from micro, 0.6% from macro, and 94% from external), whilst indirect rates were estimated at 2.4 ± 0.20 …

Locality-Aware Tail Node Embeddings On Homogeneous And Heterogeneous Networks, Zemin Liu, Yuan Fang, Wentao Zhang, Xinming Zhang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

While the state-of-the-art network embedding approaches often learn high-quality embeddings for high-degree nodes with abundant structural connectivity, the quality of the embeddings for low-degree or nodes is often suboptimal due to their limited structural connectivity. While many real-world networks are long-tailed, to date little effort has been devoted to tail node embeddings. In this article, we formulate the goal of learning tail node embeddings as a problem, given the few links on each tail node. In particular, since each node resides in its own local context, we personalize the regression model for each tail node. To reduce overfitting in the …

Posmlp-Video: Spatial And Temporal Relative Position Encoding For Efficient Video Recognition, Yanbin Hao, Diansong Zhou, Zhicai Wang, Chong-Wah Ngo, Xiangnan He, Meng Wang

Research Collection School Of Computing and Information Systems

In recent years, vision Transformers and MLPs have demonstrated remarkable performance in image understanding tasks. However, their inherently dense computational operators, such as self-attention and token-mixing layers, pose significant challenges when applied to spatio-temporal video data. To address this gap, we propose PosMLP-Video, a lightweight yet powerful MLP-like backbone for video recognition. Instead of dense operators, we use efficient relative positional encoding (RPE) to build pairwise token relations, leveraging small-sized parameterized relative position biases to obtain each relation score. Specifically, to enable spatio-temporal modeling, we extend the image PosMLP’s positional gating unit to temporal, spatial, and spatio-temporal variants, namely PoTGU, …

Rfssa: An R Package For Functional Singular Spectrum Analysis, Hossein Haghbin, Jordan Trinka, Mehdi Maadooliat

Mathematical and Statistical Science Faculty Research and Publications

Functional Singular Spectrum Analysis (FSSA) is a non-parametric approach for analyzing Functional Time Series (FTS) and Multivariate FTS (MFTS) data. This paper introduces Rfssa, an R package that addresses implementing FSSA for FTS and MFTS data types. Rfssa provides a flexible container, the funts class, for FTS/MFTS data observed on one-dimensional or multi-dimensional domains. It accepts arbitrary basis systems and offers powerful graphical tools for visualizing time-varying features and pattern changes. The package incorporates two forecasting algorithms for FTS data. Developed using object-oriented programming and Rcpp/RcppArmadillo, Rfssa ensures computational efficiency. The paper covers theoretical background, technical details, usage examples, and …

Ethical Considerations Toward Protestware, Marc Cheong, Raula Kula, Christoph Treude

Research Collection School Of Computing and Information Systems

This article looks into possible scenarios where developers might consider turning their free and open source software into protestware. Using different frameworks commonly used in artificial intelligence (AI) ethics, we extend the applications of AI ethics to the study of protestware.

To Protect Or To Hide: An Investigation On Corporate Redacted Disclosure Motives Under New Fast Act Regulation, Yan Ma, Qian Mao, Nan Hu

Research Collection School Of Computing and Information Systems

China adopted amendments allowing companies to redact filings without prior approval in 2016. Leveraging this change as a quasi-nature experiment, we explore whether managers utilize redacted information to withhold bad information in the more lenient regulatory environment. Our investigation uncovers a significant shift in managerial behavior: Since 2016, managers incline to employ redactions to obscure negative news rather than safeguarding proprietary data. Furthermore, we find that the poorer firm performance and a higher cost of equity are associated with the redacted disclosures after 2016, suggesting that investors perceive an increase in firm-specific risk attributed to withholding bad news through redactions.

Smart Hpa: A Resource-Efficient Horizontal Pod Auto-Scaler For Microservice Architectures, Hussain Ahmad, Christoph Treude, Markus Wagner, Claudia Szabo

Research Collection School Of Computing and Information Systems

Microservice architectures have gained prominence in both academia and industry, offering enhanced agility, reusability, and scalability. To simplify scaling operations in microservice architectures, container orchestration platforms such as Kubernetes feature Horizontal Pod Auto-scalers (HPAs) designed to adjust the resources of microservices to accommodate fluctuating workloads. However, existing HPAs are not suitable for resourceconstrained environments, as they make scaling decisions based on the individual resource capacities of microservices, leading to service unavailability and performance degradation. Furthermore, HPA architectures exhibit several issues, including inefficient data processing and a lack of coordinated scaling operations. To address these concerns, we propose Smart HPA, a …

Violet: Visual Analytics For Explainable Quantum Neural Networks, Shaolun Ruan, Zhiding Liang, Qiang Guan, Paul Robert Griffin, Xiaolin Wen, Yanna Lin, Yong Wang

Research Collection School Of Computing and Information Systems

With the rapid development of Quantum Machine Learning, quantum neural networks (QNN) have experienced great advancement in the past few years, harnessing the advantages of quantum computing to significantly speed up classical machine learning tasks. Despite their increasing popularity, the quantum neural network is quite counter-intuitive and difficult to understand, due to their unique quantum-specific layers (e.g., data encoding and measurement) in their architecture. It prevents QNN users and researchers from effectively understanding its inner workings and exploring the model training status. To fill the research gap, we propose VIOLET , a novel visual analytics approach to improve the explainability …

Dappscan: Building Large-Scale Datasets For Smart Contract Weaknesses In Dapp Projects, Zibin Zheng, Jianzhong Su, Jiachi Chen, David Lo, Zhijie Zhong, Mingxi Ye

Research Collection School Of Computing and Information Systems

The Smart Contract Weakness Classification Registry (SWC Registry) is a widely recognized list of smart contract weaknesses specific to the Ethereum platform. Despite the SWC Registry not being updated with new entries since 2020, the sustained development of smart contract analysis tools for detecting SWC-listed weaknesses highlights their ongoing significance in the field. However, evaluating these tools has proven challenging due to the absence of a large, unbiased, real-world dataset. To address this problem, we aim to build a large-scale SWC weakness dataset from real-world DApp projects. We recruited 22 participants and spent 44 person-months analyzing 1,199 open-source audit reports …

Inceptionnext: When Inception Meets Convnext, Weihao Yu, Pan Zhou, Shuicheng Yan, Xinchao Wang

Research Collection School Of Computing and Information Systems

Inspired by the long-range modeling ability of ViTs, large-kernel convolutions are widely studied and adopted recently to enlarge the receptive field and improve model performance, like the remarkable work ConvNeXt which employs 7×7 depthwise convolution. Although such depthwise operator only consumes a few FLOPs, it largely harms the model efficiency on powerful computing devices due to the high memory access costs. For example, ConvNeXtT has similar FLOPs with ResNet-50 but only achieves ∼ 60% throughputs when trained on A100 GPUs with full precision. Although reducing the kernel size of ConvNeXt can improve speed, it results in significant performance degradation, which …