Physical Sciences and Mathematics Commons^™

My First Conversation With Chatgpt (February 22, 2023): Origins Of A Generative Dialogue, David Smith

Publications and Research

This working paper presents the first recorded interaction between the author and the generative AI system ChatGPT, written on February 22, 2023 during the initial weeks of a faculty sabbatical in Boston. The document preserves a complete and unedited transcript of an exploratory conversation conducted without predetermined research aims, marking the author’s first encounter with a large-language-model conversational interface. Although the exchange includes creative experimentation—including musical and poetic prompts—the discussion remains informal and wide-ranging, and no theoretical framework is articulated at this stage. Rather, this transcript is published as primary-source material documenting the moment of discovery and experimentation that precedes …

The Study Of Hybrid Nanostructures For Optoelectronics, Alex Davis, Khaoula Dehhou

Publications and Research

Hybrid nanostructures that combine zero-dimensional (0D) quantum dots (QDs) with two-dimensional (2D) transition-metal dichalcogenides (TMDs) are promising building blocks for next-generation optoelectronic devices, such as highly sensitive photodetectors, light-emitting diodes, and tunable light sources. These hybrid systems take advantage of the strong, size-dependent emission of colloidal QDs and the high carrier mobility and tunable band structure of 2D materials. In this research, we investigate hybrid structures composed of colloidal CdSe/ZnS, CuInS/ZnS, and PbS QDs integrated with indium selenide (InSe), a TMD known for its exceptional electron mobility and robust photoluminescence in few-layer form. The goal is to understand how interfacial …

Evaluating How New York City’S Recycling And Organic Waste Systems Reduce Resource Waste And Methane Emissions, Zihan Cao

Publications and Research

This study analyzes New York City’s waste management systems, with a focus on food waste diversion, composting, and renewable energy initiatives, to assess their role in methane emission reduction and resource efficiency. The analysis highlights the relative effectiveness of different waste management pathways and suggests that community-scale composting and decentralized anaerobic digestion are associated with substantial methane reductions relative to cost.

Extensions Of Weighted Integral Inequalities For Ga-Convex Functions In Connection With Fejér’S Result, Muhammad Amer Latif

Publications and Research

No abstract provided.

Early Conceptual Sketches Of Blended Reality And The Precursor To The Bbs Quad (2022), David Smith

Publications and Research

This document contains two original hand-drawn conceptual sketches created in early 2022, representing the earliest visual formulations of what would later evolve into the Balanced Blended Space (BBS) framework. The drawings predate my first conversations with ChatGPT and were produced as part of my independent sabbatical research into blended environments, mediated performance, and human–machine interaction.

The first drawing examines human–computational mediation, perception, and internal mapping. The second sketch—later referred to informally as the “BBS Quad”—extends this idea by reconciling cognition–computation symmetry with physical–virtual spatial relationships. Published together, these images document the conceptual foundations of the BBS framework prior to its …

Speculative Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian Ph.D., Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code---supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations. Our key insight is that, while DL programs typically execute sequentially, hybridizing imperative DL code resembles parallelizing sequential code in traditional systems. Inspired by this, we …

Speculative Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian Ph.D., Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code---supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations. Our key insight is that, while DL programs typically execute sequentially, hybridizing imperative DL code resembles parallelizing sequential code in traditional systems. Inspired by this, we …

Speculative Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi T. Khatchadourian Ph.D., Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code---supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations. Our key insight is that, while DL programs typically execute sequentially, hybridizing imperative DL code resembles parallelizing sequential code in traditional systems. Inspired by this, we …

Electric-Field Tunable Magnetoexcitons In Xenes/Hbn/Tmdc, Xenes/Hbn/Bp, And Xenes/Hbn/Tmtc Heterostructures, Roman Ya. Kezerashvili, Anastasia Spiridonova, Klaus Ziegler

Publications and Research

In this work, we propose novel van der Waals heterostructures composed of Xenes, transition metal dichalcogenides (TMDCs), phosphor ene, and transition metal trichalcogenides (TMTCs), which are separated by insulating hexagonal boron nitride (hBN) layers. We theoreti cally investigate the behavior of Rydberg indirect excitons in Xenes/hBN/TMDC, Xenes/hBN/phosphorene and Xenes/hBN/TMTC heterostructures subjected to parallel external electric and magnetic fields that are oriented perpendicular to the layers. By incorporating both isotropic and anisotropic materials, we demonstrate that excitonic properties can be effectively tuned through the external field strengths and the heterostructure design. Our results show that the exciton reduced mass and the …

Art Forms: Born Out Of Necessity, Urmi Duttagupta

Publications and Research

This collection of artworks expresses the artist’s profound passion for mathematics and life’s complex balance. As a female mathematician, she uses artistic expression and intricate patterns to evoke mathematical beauty and feminine strength, celebrating the presence and resilience of women in mathematics.

Major, Trace And Rare Earth Element Concentrations In Nist Standard Reference Material® 1646a Analyzed By Als Global Using Method Me-Ms61r, Jane L. Alexander

Publications and Research

This technical note compares analytical results obtained from ALS Global Method ME-MS61r with the certified and previously published results for NIST Standard Reference Material® 1646a, Estuarine Sediment. The results are grouped into major, trace and rare earth elements, and focus on elements used by sedimentary geochemists, for example in studies of sediment provenance. The results from ALS Global Method ME-MS61r are internally consistent and generally robust when compared with published analyses. However, acid digestion may not completely dissolve resistant minerals, leading to under-reporting of some elements such as Ti, Cr, Zr, heavy REE and Y.

Advanced Robotic Multimodal Imaging With Real-Time Motion Compensation For Dynamic Structural Health Monitoring, George Papaioannou, Christos Mitrogiannis, Mark Schweitzer, Maria Pappa, Pegah Khosravi, Apostolos Karantanas, Chris Ruberg, Shawn Owens, Nikolaos Michailidis

Publications and Research

Modern composite materials promise superior performance and load-bearing capabilities, yet evaluating their structural integrity remains challenging. Current testing methods, such as visual, thermographic, ultrasonic, optical, electromagnetic, terahertz, shearography, X-ray, and neutron imaging, are hampered by long scan durations, limited field of view, suboptimal accuracy, and high costs, particularly when applied to large structures.

This paper addresses these issues by introducing a novel robotic multimodal imaging system that overcomes the limitations of traditional methods. This system dynamically captures both static and dynamic properties of materials using advanced motion compensation techniques. By integrating multiple radiographic modalities into a coordinated robotic platform, it …

Improved Rational Approximations Of Pi, Nelson Carella

Publications and Research

The irrationality exponent of a real number α is defined by maximal value μ(α) ≥ 1 such that the inequality |p/q − α| < 1/qμ(α) is true for finitely many rational approxi- mations by relatively prime pairs p/q. The earliest result introduced the upper bound μ(π) ≤ 42 for the irrationality exponent of π. Subsequently, it was improved by many authors. The more recent result introduced the improved upper bound μ(π) ≤ 7.6063. This note introduces an improved irrationality exponent μ(π) ≤ 3. The optimal value is expected to be μ(π) = 2.

"Chatgpt Told Me To Say It": Ai Chatbots And Class Participation Apprehension In University Students, Daisuke Akiba

Publications and Research

The growing prevalence of AI chatbots in everyday life has prompted educators to explore their potential applications in promoting student success, including support for classroom engagement and communication. This exploratory study emerged from semester-long observations of class participation apprehensions in an introductory educational psychology course, examining how chatbots might scaffold students toward active and independent classroom contribution. Four students experiencing situational participation anxiety voluntarily participated in a pilot intervention using AI chatbots as virtual peer partners. Following comprehensive training in AI use and prompt design given to the entire class, participants employed systematic consultation frameworks for managing classroom discourse trepidations. …

Can We Discover Physical Models Using Machine Learning? A Case Study Of Galaxy Sizes, Festa Buçinca-Çupallari, Ariyeh Maller, Viviana Acquaviva, Austen Gabrielpillai, Rachel S. Somerville

Publications and Research

We explore the ability of machine learning methods to discover underlying equations of physics by searching for the equations governing galaxy size in a semianalytic model. This case study allows us to evaluate the process as we know the ground truth. We find that we fail to find an equation to predict galaxy size on the entire data set, but are successful when we separate out disk galaxies where we expect the physics driving galaxy size to be different than in bulge-dominated systems. We are also able to find an equation for bulge size, but not without adding an additional …

Full-Stack Web Applications: Infrastructure, Development Pipelines & Devsecops, Yassine Chahid, Patrick Slattery

Publications and Research

This research explores emerging development methodologies and technologies which facilitate the deployment and maintenance of software applications. It evaluates architectural styles for the development of software such as monolithic (legacy) and microservice models, with a focus on their key differences such as scalability or project structure through to the development of an application. By examining methodologies such as Agile and continuous integration/continuous development pipelines along with the deployment tools Docker and Git for version/release control, the study analyzes how these innovations speed up development, improve existing practices, and serve as the foundation for development operations. Cloud solutions for tasks such …

Proceedings Of The Cuny Games Conference 11.0, Robert O. Duncan, Grace Axler-Diperte, Joe Bisz, Christina Boyle, Devorah Kletenik, Carolyn Stallard

Publications and Research

The CUNY Games Conference combines workshops, idea exchanges, interactive participant presentations, playtesting, and playing tabletop games into a one-day online event to promote and discuss game-based learning. The conference focuses on creative pedagogy, such as playful learning activities or games, that teachers can use in the classroom every day. The conference is online and features interactive presentations by attendees, informal idea exchange sessions, and workshops by the conference organizers.

Arts-Based Sustainability: From New York To Malawi, Martha B. Lerski

Publications and Research

Recognizing that libraries serve multiple constituencies and subject areas, this chapter documents and advocates for development of transdisciplinary arts-based research (ABR) and culture-related projects linked to environmental challenges. Libraries contribute collections and spaces, as well as the research of library and information scientists. Libraries are currently among invisible contributors to sustainability planning and services. The chapter will link this invisibility to the value of what visual arts refer to as negative space elements in subjects ranging from traditional ecological knowledge to environmental science. Library collections, projects, and research contribute to education for sustainable development (ESD) as required to achieve the …

Integrating Universal Generative Ai Platforms In Educational Labs To Foster Critical Thinking And Digital Literacy, Vasiliy S. Znamenskiy, Joel Hernandez, Rafael Niyazov

Publications and Research

This study investigates the educational potential of generative artificial intelligence (GenAI) platforms based on large language models (LLMs), such as ChatGPT, Claude, and Gemini, as tools for studentcentred learning. Recognizing the current limitations of GenAI, particularly its propensity for generating inaccurate or misleading information—the paper proposes a novel instructional strategy: an interdisciplinary laboratory designed to foster critical evaluation of GenAI-generated outputs. In this pedagogical model, students engage with GenAI systems by posing questions or solving problems drawn from topics they have already studied and understand. Equipped with correct answers, students are positioned to assess the accuracy and relevance of AI-generated …

Quantum-Enhanced Training Of Large Language Models: A Hybrid Approach, Nan Wu, Fangmin Song, Xiangdong Li

Publications and Research

The training of large language models (LLMs) presents significant computational challenges, particularly regarding efficient convergence. This paper presents a hybrid quantum-classical framework designed to address the significant computational challenges associated with training large language models (LLMs). By integrating quantum computing principles superposition, entanglement, and tunneling with classical deep learning methods, we propose an approach to accelerate convergence, enhance optimization efficiency, and improve model generalization. Specifically, quantum feature mapping is employed to project classical data into high-dimensional Hilbert spaces, facilitating more expressive data representations. Quantum-assisted optimization algorithms, such as Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), efficiently navigate …

Automation Of Javanese Shadow Puppets Using Machine Control, Kristian Rice, Yinson Tso, Mukhammadali Yuldoshev

Publications and Research

The virtualization of Javanese shadow puppetry (Wayang Kulit) offers a unique opportunity to preserve and revitalize traditional performance art through immersive digital platforms. This project explores the development of a virtual Wayang Kulit experience using real-time 3D engines like Unity/Unreal Engine while focusing on simulating the mechanics and aesthetics of shadow puppet performance. The puppets are designed using detailed 2D planes and rigged with skeletal systems to reflect the stylized motion of traditional puppetry. An aspect of this project is integrating an AI-driven control system that autonomously animates the puppets, learning from recorded puppeteer performances to replicate gesture, rhythm, and …

Bayesian Statistics: Origins And Applications, Evelyn Pulla

Publications and Research

Bayesian Statistics applies Bayes' Theorem to update beliefs through new evidence. In this project, I explored how Bayesian Statistics applies into real supporting decision-making under uncertainty. By solving problems using data, I was able to realize how prior knowledge and evidence collaborate to make better conclusions. The project also demonstrates how Bayesian reasoning corrects our intuition to make decisions based on logical reasoning. Through this project, I was able to learn why using probability to make informed decisions matters both in science and real life.

Symmetry Induced Pairing In Dark Excitonic Condensate At Finite Temperature, Adham Alkady, Anatoly Kuklov

Publications and Research

Bose Einstein condensate of dark intervalley excitons must be inherently multi-component because of crystalline symmetries. Since valleys hosting such excitons are separated by large quasi-momenta, a minimal inter-component Josephson-type coupling can only be established between pairs of excitons from the time-reversed valleys. As a result, a paired condensate can emerge at finite temperature, that is, the off-diagonal order exists for the pairs from the time-reversed valleys, while the individual valleys are disordered. This prediction follows from the elementary mean field analysis regardless of the dimensionality. However, as Monte Carlo simulations show, no such a phase exists in 3D crystals. Instead, …

Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …

Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …

Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …

Theoretical Proof Of And Proposed Experimental Search For The Ground Triplet State Of A Wigner-Regime Two-Electron ‘Artificial Atom’ In A Magnetic Field, Marlina Slamet, Viraht Sahni

Publications and Research

It is experimentally established that there is no ground triplet state of the natural He atom. There is also no exact analytical solution to the Schrödinger equation corresponding to this state. For a two-dimensional two-electron ‘artificial atom’ or a semiconductor quantum dot in a magnetic field, as described by the Schrödinger–Pauli equation, we provide theoretical proof of the existence of a ground triplet state by deriving an exact analytical correlated wave function solution to the equation. The state exists in the Wigner high-electron-correlation regime. We further explain that the solution satisfies all requisite symmetry and electron coalescence constraints of …

Full-Stack Web Applications: Industry Standard Frameworks, Libraries & Technologies, Yassine Chahid, Patrick Slattery

Publications and Research

This research explores emerging full-stack web development technologies across front-end, back-end, and DevSecOps domains. It evaluates modern tools including Django, React, and TypeScript—focusing on their key features such as compile-time error checking—through to the development of a web application. By examining documentation for the frameworks Node.js, Next.js, Tailwind CSS, and others, along with the deployment tools Docker and Git for version/release control, the study analyzes how these innovations speed up development, improve existing practices, and have often replaced older technologies. Cloud solutions for tasks such as authentication and deployment will also be evaluated, along with various web-application technology stacks and …

Autoradai: A Versatile Artificial Intelligence Framework Validated For Detecting Extracapsular Extension In Prostate Cancer, Pegah Khosravi, Shady Saikali, Abolfazl Alipour, Saber Mohammadi, Maxwell Boger, Dalanda M. Diallo, Christopher J. Smith, Marcio C. Moschovas, Iman Hajirasouliha, Andrew J. Hung, Srirama S. Venkataraman, Vipul Patel

Publications and Research

Preoperative identification of extracapsular extension (ECE) in prostate cancer (PCa) is crucial for effective treatment planning, as ECE presence significantly increases the risk of positive surgical margins and early biochemical recurrence following radical prostatectomy. AutoRadAI, an innovative artificial intelligence (AI) framework, was developed to address this clinical challenge while demonstrating broader potential for diverse medical imaging applications. The framework integrates T2-weighted MRI data with histopathology annotations, leveraging a dual convolutional neural network (multi-CNN) architecture. AutoRadAI comprises two key components: ProSliceFinder, which isolates prostate-relevant MRI slices, and ExCapNet, which evaluates ECE likelihood at the patient level. The system was trained and …

Primates From Senèze, Eric Delson

Publications and Research

Senèze has yielded two primate fossils, neither from the recent field work. A partial ulna can be identified as cf. Macaca sylvanus. A nearly complete female skull is the holotype of Dolichopithecus arvernensis Depéret, 1928, now included in Paradolichopithecus. Emended and differential diagnoses are provided for this genus and species for the first time, and the Senèze skull is described and briefly compared to those of other species of the genus. Dental and cranial measurements are provided, along with comparative data. The macaque and Paradolichopithecus are moderately to highly terrestrial, respectively, and they suggest a woodland to more open …