Digital Commons Network^™

On Adaptivity And Randomness For Streaming Algorithms, Manuel Stoeckl

Dartmouth College Ph.D Dissertations

A streaming algorithm has a limited amount of memory and reads a long sequence (data stream) of input elements, one by one, and computes an output depending on the input. Such algorithms may be used in an online fashion, producing a sequence of intermediate outputs corresponding to the prefixes of the data stream. Adversarially robust streaming algorithms are required to give correct outputs with a desired probability even when the data stream is adaptively generated by an adversary that can see all intermediate outputs of the algorithm. This thesis binds together research on a variety of problems related to the …

The First Variational Formula, The Phase Space Of Solutions, And The Ostrogradsky Formalism, Matthew Pontius, Drew Watson

Physics Capstone Projects

We consider Lagrangians for classical mechanics which depend upon an arbitrary number of time derivatives of the configuration variables. From the boundary term in the first variation of the Lagrangian we derive the Ostrogradsky formulas which define the Hamiltonian formulation of mechanical systems.

Fluorescence Microscopy With Deep Uv, Near Uv, And Visible Excitation For In Situ Detection Of Microorganisms, Noel Case, Nikki Johnston, Jay Nadeau

Physics Faculty Publications and Presentations

We report a simple, inexpensive design of a fluorescence microscope with light-emitting diode (LED) excitation for detection of labeled and unlabeled microorganisms in mineral substrates. The use of deep UV (DUV) excitation with visible emission requires no specialized optics or slides and can be implemented easily and inexpensively using an oblique illumination geometry. DUV excitation (<280 >nm) is preferable to near UV (365 nm) for avoidance of mineral autofluorescence. When excited with DUV, unpigmented bacteria show two emission peaks: one in the near UV ∼320 nm, corresponding to proteins, and another peak in the blue to green range, corresponding to …

Artificial General Intelligence And The Mind-Body Problem: Exploring The Computability Of Simulated Human Intelligence In Light Of The Immaterial Mind, Caleb Parks

Senior Honors Theses

In this thesis I explore whether achieving artificial general intelligence (AGI) through simulating the human brain is theoretically possible. Because of the scientific community’s predominantly physicalist outlook on the mind-body problem, AGI research may be limited by erroneous foundational presuppositions. Arguments from linguistics and mathematics demonstrate that the human intellect is partially immaterial, opening the door for novel analysis of the mind’s simulability. I categorize mind-body problem philosophies in a manner relevant to computer science based upon state transitions, and determine their ramifications on mind-simulation. Finally, I demonstrate how classical architectures cannot resolve so-called Gödel statements, discuss why this inability …

Effects Of Heterogeneous Data Sets And Time-Lag Measurement Techniques On Cosmological Parameter Constraints From Mg Ii And C Iv Reverberation-Mapped Quasar Data, Shulei Cao, Michal Zajaček, Bożena Czerny, Swayamtrupta Panda, Bharat Ratra

Physics Faculty Publications and Presentations

Previously, we demonstrated that Mg II and C IV reverberation-mapped quasars (RM QSOs) are standardizable and that the cosmological parameters inferred using the broad-line region radius–luminosity (R–L) relation are consistent with those determined from better-established cosmological probes. With more data expected from ongoing and future spectroscopic and photometric surveys, it is imperative to examine how new QSO data sets of varied quality, with their own specific luminosity and time-delay distributions, can be best used to determine more restrictive cosmological parameter constraints. In this study, we test the effect of adding 25 OzDES Mg II RM QSOs as well …

Smu Libraries – An Enabling Partner In Ai Information Literacy, Samantha Seah, Zhe Benedict Yeo, Lukas Tschopp

Research Collection Library

SMU Libraries plays a pivotal role in advancing AI information literacy within the larger need for digital literacy skills in the SMU community. In this presentation, participants will get an overview of SMU Libraries' engagement and partnerships with the academic community and will showcase initiatives and resources supporting AI literacy. This includes a discussion of insights from the scholarly literature, research findings and critical perspectives to inform teaching and learning practices related to AI. Speakers will share SMU Libraries’ contributions towards awareness and adoption of AI through a portfolio of successful collaborations and initiatives with partners and stakeholders within and …

Regional Differences Of Climate Change In Maine: Flow Rates, Precipitation, And Snowpack, Caitlyn Rose Daigle, Alex James Debo, Jason Daniel Moore, Lucky Mourredes, Cara Wren Perry, Eme L. Saverese, Kennedy Grace Todd, Sophia Lydia Winters

Research Learning Experiences (RLEs)

● Maine winters are changing rapidly, associated with changes in climate.

● These climate-linked changes are implicated in flooding, changes in snowpack, and changes in flow regimes in Maine.

● In this study, four different regions in Maine were analyzed to evaluate changes over time in snowpack, river ice, fall-through-spring precipitation,February Snowpack water equivalent

Seasonal Variability In Peak Flow Of Maine Rivers, Brianna L. Benson, Salfa Hendrix, Christopher Houdeshell, Emma Mae Hovencamp, Kaylee M. Perron, Wyeth Bird Purkiss

Research Learning Experiences (RLEs)

Questions and Hypotheses

• How has the timing of peak flow changed over time? ○ Hypothesis: Peak flow has moved earlier in the spring due to a warming climate melting snow earlier.

• How has the variation of flow changed over time?

○ Hypothesis: Flow has grown more

variable in more recent years due to an increase in more variable precipitation patterns, especially in the spring months.

Comparison Of Emissions Across Tobacco Products: A Slippery Slope In Tobacco Control, Ahmad El-Hellani, Ayomipo Adeniji, Hanno C. Erythropel, Thomas Lamb, Qixin Wang, Vladimir Mikheev, Robert Strongin, Multiple Additional Authors

Chemistry Faculty Publications and Presentations

In this narrative review, we highlight the challenges of comparing emissions from different tobacco products under controlled laboratory settings (using smoking/ vaping machines). We focus on tobacco products that generate inhalable smoke or aerosol, such as cigarettes, cigars, hookah, electronic cigarettes, and heated tobacco products. We discuss challenges associated with sample generation including variability of smoking/vaping machines, lack of standardized adaptors that connect smoking/vaping machines to different tobacco products, puffing protocols that are not representative of actual use, and sample generation session length (minutes or number of puffs) that depends on product characteristics. We also discuss the challenges of physically …

Navigating The Maze: The Role Of Pre-Enrollment Socio-Cultural And Institutional Factors In Higher Education In The Age Of Ai, Emily Barnes, James Hutson

Faculty Scholarship

This article explores the complex interplay between pre-enrollment socio-cultural and institutional factors and their impact on the higher education landscape. It challenges traditional metrics of academic achievement, presenting a nuanced perspective on student success that emphasizes the importance of socio-economic backgrounds, cultural capital, and K-12 education quality. The analysis extends to the significant role of institutional attributes in shaping student readiness and decision-making processes. The study advocates for the integration of artificial intelligence (AI)-driven assessments by higher education institutions to cater to the diverse needs of the student body, promoting an inclusive and supportive learning environment. Anchored in an extensive …

Elevating Academic Administration: A Comprehensive Faculty Dashboard For Tracking Student Evaluations And Research, Musa M. Azeem

Senior Theses

The USC Faculty Dashboard is a web application designed to revolutionize how department heads, professors, and instructors monitor progress and make decisions, providing a centralized hub for efficient data storage and analysis. Currently, there’s a gap in tools tailored for department heads to concisely manage the performance of their department, which our platform aims to fill. The USC Faculty Dashboard offers easy access to upload and view student evaluation and research information, empowering department heads to evaluate the performance of faculty members and seamlessly track their research grants, publications, and expenditures. Furthermore, professors and instructors gain personalized performance analysis tools, …

A New Canvas Of Learning: Enhancing Formal Analysis Skills In Ap Art History Through Ai-Generated Islamic Art, Krista Carpino, James Hutson

Faculty Scholarship

This study explores the use of AI art generators to enhance formal analysis skills in AP Art History students, with a focus on Islamic Art and Architecture. Students, often entering the course with high academic achievements, find the unique challenge of articulating detailed visual descriptions of artworks. The study’s approach involves using AI image-generation websites, like wepik.com, where students create AI images resembling Islamic artworks studied in class. This method aims to refine their descriptive skills, focusing on visual evidence rather than relying on identifying details. The choice of Islamic Art, markedly different from other historical periods covered in the …

Synthesis Of An Anti-Lysozyme Antibody Tagged With Gold Or Silver Nanoparticle For Faster Western Blot, Harunobu Kato

Chemistry Senior Theses

Western blot is an important protein analysis assay enabling protein detection by visualizing the antibody specific antigen-antibody interaction. As a commonly used assay, western blot is taught in upper-level biochemistry and molecular biology students. However, due to its exhaustive process and high cost of antibodies, a modification to western blot is proposed using gold or silver nanoparticles as a tag to the primary antibody for lysozyme. In this experiment, invisible anti-lysozyme antibodies were conjugated to colloidal gold and silver nanoparticles via photochemical immobilization technique, effectively facilitating a physically observable band on the western blot. To eliminate false positives by interactions …

Paleozoic Evolution Of The Yukon-Tanana Terrane Of The North American Cordillera, Nw British Columbia, R. Soucy La Roche, A. Zagorevski, N. L. Joyce, J. L. Crowley

Geosciences Faculty Publications and Presentations

The origins and primary relationships between tectono-stratigraphic units are fundamental to the terrane concept in accretionary orogens, but they are challenging to assess in metamorphic terranes. In NW British Columbia, three tectonically bounded metamorphic suites of the Yukon-Tanana terrane formed in distinct tectonic settings, based on high-spatial-resolution geochronology and immobile trace-element geochemistry. The Florence Range suite comprises late Neoproterozoic or younger to pre–latest Devonian metasedimentary rocks derived from continental crust, 360 ± 4 Ma calc-alkaline intermediate orthogneiss, and 357 ± 4 Ma amphibolite with oceanic-island basalt composition, consistent with rifting of a continental margin. The detrital signature is dominated by …

A Computer Vision Solution To Cross-Cultural Food Image Classification And Nutrition Logging​, Rohan Sethi, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

The US is a culturally and ethnically diverse country, and with this diversity comes a myriad of cuisines and eating habits that expand well beyond that of western culture. Each of these meals have their own good and bad effects when it comes to the nutritional value and its potential impact on human health. Thus, there is a greater need for people to be able to access the nutritional profile of their diverse daily meals and better manage their health. A revolutionary solution to democratize food image classification and nutritional logging is using deep learning to extract that information from …

Global Challenges In Accessing Mental Health Services And Addressing The Impact Of Alzheimer's Disease And Depression, Padmapriya Velupillai Meikandan

Master's Theses (2009 -)

This research project focuses on developing a quantum sensing system that can detect biomarkers associated with health disorders, like Alzheimer’s and depression. Our goal is to create a sensitive and highly selective quantum sensing device using a diamond nitrogen vacancy (NV) center. To train and test our quantum machine learning algorithms we will preprocess data from the available Human Connectome Project dataset. This dataset forms the basis of our quantum-based methods. The core of our project revolves around developing quantum machine learning algorithms that utilize techniques such as Support Vector Machines and neural networks to diagnose health disorders using data …

Integrating Drone And Satellite Imaging With Machine Learning For Green Stormwater Infrastructure Condition Assessments, Matthew Dupasquier

Master's Theses (2009 -)

Green Stormwater Infrastructure (GSI) has been increasingly utilized to improve urban stormwater management strategies. However, the performance and utility of GSI decrease over time if the infrastructure is not properly maintained. In recent history, the intrinsic operations and maintenance costs associated with the complex networks of new infrastructure have placed a burden on municipalities, ultimately prohibiting many from reaching the full extent of their stormwater management goals. One way for cities to achieve cost savings is through automated monitoring that can quickly assess the condition of GSI assets; however, existing cost-effective technologies are limited. Drones and satellites may be able …

Advanced Machine Learning Approaches For Predicting Mental Health Disorders Following Long Covid Diagnosis, Manoj Purohit

Master's Theses (2009 -)

After the global spread of COVID-19, the enduring effects of Long COVID and its health implications have emerged as a significant global issue, affecting people worldwide. The lingering symptoms post a COVID-19 infection can significantly affect individuals who had previously contracted the virus, exerting considerable influence over their mental well-being. Prolonged recuperation associated with Long COVID has been connected with the emergence of symptoms such as depression and anxiety, all of which can have adverse effects on emotional health. This project delves into an in-depth analysis of healthcare data pertaining to Long COVID from the Froedtert Health (FH) Medical System …

Discovering Significant Topics From Legal Decisions With Selective Inference, Jerrold Tsin Howe Soh

Research Collection Yong Pung How School Of Law

We propose and evaluate an automated pipeline for discovering significant topics from legal decision texts by passing features synthesized with topic models through penalized regressions and post-selection significance tests. The method identifies case topics significantly correlated with outcomes, topic-word distributions which can be manually interpreted to gain insights about significant topics, and case-topic weights which can be used to identify representative cases for each topic. We demonstrate the method on a new dataset of domain name disputes and a canonical dataset of European Court of Human Rights violation cases. Topic models based on latent semantic analysis as well as language …

Multi-Aspect Rule-Based Ai: Methods, Taxonomy, Challenges And Directions Towards Automation, Intelligence And Transparent Cybersecurity Modeling For Critical Infrastructures, Iqbal H. Sarker, Helge Janicke, Mohamed A. Ferrag, Alsharif Abuadbba

Research outputs 2022 to 2026

Critical infrastructure (CI) typically refers to the essential physical and virtual systems, assets, and services that are vital for the functioning and well-being of a society, economy, or nation. However, the rapid proliferation and dynamism of today's cyber threats in digital environments may disrupt CI functionalities, which would have a debilitating impact on public safety, economic stability, and national security. This has led to much interest in effective cybersecurity solutions regarding automation and intelligent decision-making, where AI-based modeling is potentially significant. In this paper, we take into account “Rule-based AI” rather than other black-box solutions since model transparency, i.e., human …

Matrix Profile Data Mining For Bgp Anomaly Detection, Ben A. Scott, Michael N. Johnstone, Patryk Szewczyk, Steven Richardson

Research outputs 2022 to 2026

The Border Gateway Protocol (BGP), acting as the communication protocol that binds the Internet, remains vulnerable despite Internet security advancements. This is not surprising, as the Internet was not designed to be resilient to cyber-attacks, therefore the detection of anomalous activity was not of prime importance to the Internet creators. Detection of BGP anomalies can potentially provide network operators with an early warning system to focus on protecting networks, systems, and infrastructure from significant impact, improve security posture and resilience, while ultimately contributing to a secure global Internet environment. In this paper, we present a novel technique for the detection …

Accurate Characterization Of Binding Kinetics And Allosteric Mechanisms For The Hsp90 Chaperone Inhibitors Using Ai-Augmented Integrative Biophysical Studies, Chao Xu, Xianglei Zhang, Lianghao Zhao, Gennady M. Verkhivker, Fang Bai

Mathematics, Physics, and Computer Science Faculty Articles and Research

The binding kinetics of drugs to their targets are gradually being recognized as a crucial indicator of the efficacy of drugs in vivo, leading to the development of various computational methods for predicting the binding kinetics in recent years. However, compared with the prediction of binding affinity, the underlying structure and dynamic determinants of binding kinetics are more complicated. Efficient and accurate methods for predicting binding kinetics are still lacking. In this study, quantitative structure–kinetics relationship (QSKR) models were developed using 132 inhibitors targeting the ATP binding domain of heat shock protein 90α (HSP90α) to predict the dissociation rate …

Exploring Quaternion Neural Network Loss Surfaces, Jeremiah Bill, Bruce A. Cox

Faculty Publications

This paper explores the superior performance of quaternion multi-layer perceptron (QMLP) neural networks over real-valued multi-layer perceptron (MLP) neural networks, a phenomenon that has been empirically observed but not thoroughly investigated. The study utilizes loss surface visualization and projection techniques to examine quaternion-based optimization loss surfaces for the first time. The primary contribution of this research is the statistical evidence that QMLP models yield smoother loss surfaces than real-valued neural networks, which are measured and compared using a robust quantitative measure of loss surface “goodness” based on estimates of surface curvature. Extensive computational testing validates the effectiveness of these surface …

Reevaluating The Origin Of Detectable Cataclysmic Variables In Globular Clusters: Testing The Importance Of Dynamics, Liliana Rivera Sandoval, Diogo Belloni, Miriam Ramos Arevalo

Physics and Astronomy Faculty Publications and Presentations

Based on the current detectable cataclysmic variable (CV) population in Galactic globular clusters (GCs), we show that there is not a clear relation between the number of sources per unit of mass and the stellar encounter rate, the cluster mass, or the cluster central density. If any, only in the case of core-collapsed GCs could there be an anticorrelation with the stellar encounter rate. Our findings contrast with previous studies where clear positive correlations were identified. Our results suggest that correlations between faint X-ray sources, from which often conclusions for the CV population are drawn, and the GC parameters considered …

Climate Change And Voluntary Private Land Conservation: A Case Study Of Working Lands For Wildlife, Abigail Thompson

School of Professional Studies

This case study examines the role, impact, and future of voluntary private land conservation (VPLC) programs, with a particular focus on the U.S. Department of Agriculture’s Working Lands for Wildlife (WLFW) initiative. Climate change and population growth pose a significant threat to public land conservation, making alternative methods like WLFW increasingly important. WLFW is a relatively successful and well-received program, but it is still young and comparatively smaller than other VPLC programs operated by the government. Publications by the U.S. Department of Agriculture, WLFW partner organizations, and relevant scholarly articles were utilized in order to assess WLFW’s success independently and …

A Green New England? Regional Implementation Of Grant-Based Provisions Of The Inflation Reduction Act In The Northeastern U.S., Samuel Cooper

Sustainability and Social Justice

The Inflation Reduction Act of 2022 has been described as “the most significant action Congress has taken on clean energy and climate change in the nation’s history,” totaling some $370 billion in tax credits and federal grants for everything from residential solar panels to urban forestry. As the first of its size in U.S. climate policy, the IRA has been a subject of study and debate since its introduction, but it is only in this past year that funding reporting data has become available. This thesis utilizes this federal data to produce a novel analysis of IRA implementation at the …

Environmental, Social, And Governance (Esg) And Artificial Intelligence In Finance: State-Of-The-Art And Research Takeaways, Tristan Lim

Research Collection School Of Computing and Information Systems

The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation focuses on three primary research questions: the main research themes concerning ESG and AI in finance, the evolution of research intensity and interest in these areas, and the application and evolution of AI techniques specifically in research studies within the ESG and AI in finance domain. Eight archetypical …

Editorial: Emerging On-Demand Passenger And Logistics Systems: Modelling, Optimization, And Data Analytics, Jintao Ke, Hai Wang, Neda Masoud, Maximilian Schiffer, Goncalo H. A. Correia

Research Collection School Of Computing and Information Systems

The proliferation of smart personal devices and mobile internet access has fueled numerous advancements in on-demand transportation services. These services are facilitated by online digital platforms and range from providing rides to delivering products. Their influence is transforming transportation systems and leaving a mark on changing individual mobility, activity patterns, and consumption behaviors. For instance, on-demand transportation companies such as Uber, Lyft, Grab, and DiDi have become increasingly vital for meeting urban transportation needs by connecting available drivers with passengers in real time. The recent surge in door-to-door food delivery (e.g., Uber Eats, DoorDash, Meituan); grocery delivery (e.g., Amazon Fresh, …

Marco: A Stochastic Asynchronous Concolic Explorer, Jie Hu, Yue Duan, Heng Yin

Research Collection School Of Computing and Information Systems

Concolic execution is a powerful program analysis technique for code path exploration. Despite recent advances that greatly improved the efficiency of concolic execution engines, path constraint solving remains a major bottleneck of concolic testing. An intelligent scheduler for inputs/branches becomes even more crucial. Our studies show that the previously under-studied branch-flipping policy adopted by state-of-the-art concolic execution engines has several limitations. We propose to assess each branch by its potential for new code coverage from a global view, concerning the path divergence probability at each branch. To validate this idea, we implemented a prototype Marco and evaluated it against the …

Acav: A Framework For Automatic Causality Analysis In Autonomous Vehicle Accident Recordings, Huijia Sun, Christopher M. Poskitt, Yang Sun, Jun Sun, Yuqi Chen

Research Collection School Of Computing and Information Systems

The rapid progress of autonomous vehicles (AVs) has brought the prospect of a driverless future closer than ever. Recent fatalities, however, have emphasized the importance of safety validation through large-scale testing. Multiple approaches achieve this fully automatically using high-fidelity simulators, i.e., by generating diverse driving scenarios and evaluating autonomous driving systems (ADSs) against different test oracles. While effective at finding violations, these approaches do not identify the decisions and actions that caused them -- information that is critical for improving the safety of ADSs. To address this challenge, we propose ACAV, an automated framework designed to conduct causality analysis for …