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Neural Network Belief Propagation and Ordered Statistics Decoding for Quantum Error Correction Codes
No full textIn the quest for fault-tolerant quantum computation, the delicate nature of qubits demands the need for robust quantum error correction. Quantum error correcting codes, the quantum counterpart to classical error correcting codes in information theory, have been developed to address the unique challenges quantum bits face against the noise of their environment in storing information. These codes include but are not limited to the Kitaev surface code, toric code, rotated surface code,
generalized bicycle code, and bivariate bicycle code.
Fast and reliable decoders for these quantum error correcting codes are needed to correct errors and achieve fault tolerance on these qubits. The belief propagation algorithm, which is a efficient and reliable heuristic for decoding classical low-density parity check codes, has been generalized to different types of quantum error correction codes. Paired with the ordered statistics decoding algorithm as a post-processing step from the soft outputs of the belief propagation decoder, belief propagation and ordered statistics decoding has been one of the most promising avenues of efficient
quantum error correction.
This thesis combines the state-of-the-art belief propagation and ordered statistics decoding algorithms with the exploration of the neural belief propagation algorithm to leverage classical deep learning machines to learn message passing patterns in decoding the newest quantum codes, the rotated surface code and bivariate bicycle code. The neural network exploits the local lattice structure of these codes to adjust the trainable weights and biases to more learn message update patterns more efficiently than the vanilla belief propagation algorithm.
In this thesis, we prove a new threshold for the physical error rate on the rotated surface code using a neural belief propagation algorithm and ordered statistics decoding that consistently outperforms the threshold established by the vanilla belief propagation algorithm with ordered statistics decoding. We additionally show the results for similar efforts on the bivariate bicycle codes, and discuss the difficulties in achieving a threshold using the current neural belief propagation algorithms.Computer Scienc
Alliances, Aggression, and Regional Cooperation: An Arctic Case Study on Small State Alliance Formation
No full textThis thesis proposes a new theoretical framework through which I explore the
complexities of small state alliance behavior using the Arctic as a regional case study.
My theoretical model challenges the core assumptions of traditional realist theories, and
contends that when there is an effective mechanism for regional cooperation, the impact
of aggression can be mitigated and small states are therefore less likely to pursue alliance
formation. I use a mixed-methods approach to analyze regional cooperation, Russian
aggression, and small state alliance data from 1947 to 2024, to understand the interplay of
these variables using multilevel modeling and moderation regression analyses. My results
reveal that regional cooperation, particularly through the Arctic Council, is effective at
moderating the relationship between aggression and alliance formation, showing that
when cooperation is high, it mitigates the impacts of aggression so that small states are
less likely to form new alliances despite surges in aggressive behavior. Alternatively,
when cooperation is low, escalations in aggression are far more likely to increase small
state alliance formation. The research contributes to small state alliance scholarship,
showing the importance of cooperative mechanisms as a tool for small states to leverage
as a means of securing autonomy, increasing influence, and mitigating the impacts of
aggression.Extension Studie
Land Transitions to Regenerative Vegetable Farming: Assessing Opportunities for Established Traditional Farms and Small Vegetable Growers
No full textEconomic challenges facing farmers in the United States are well-documented. Among these are systematic overproduction and low crop prices, competition on a global scale, high costs related to industrialized farming inputs and fluctuating weather (Baines, 2017). The average age of all U.S. farmers in 2022 was 58.1, up 0.6 years from 2017, highlighting a trend towards aging of farmers (USDA, 2022). Despite financial challenges of farming, there are people interested in getting into the business of farming, both in the United States and abroad (de Wit et. al., 2019). While this set of new farmers may seem a ready-made solution to the question of who will take over farms of the future, there exists a multifaceted transition gap, characterized by barriers that prevent aging farmers from exiting farming, and barriers to new farmers that would enter the profession. The most severe barrier is lack of access to land (Shute, 2011).
My research examined the mismatch between land held by traditional farms and land needed by new farmers, the economics of small-scale direct market vegetable farming, and proposed and analyzed a business model based on the success of farm incubators to address this mismatch while meeting economic needs of farmers. While a national issue, I focused on New England, where traditional farms are often on the scale of 50-100 acres, growing commodities such as hay. New direct market vegetable growers will typically only need a small plot of land on the order of one or a few acres.
This project considered a hypothetical farm that shares features of an incubator farm as an organizational model and reviewed existing benchmark studies to understand the economics of small-scale direct market vegetable farming. The central research questions were: Is it economically feasible for a traditional land-owning farm producing a commodity such as hay, to repurpose a portion of land in production and lease it to individual vegetable farmers? Could this proposed model be economically viable for both parties, and could this help reduce the barriers to entry and increase longer term viability for new farmers?
This project used scenario analysis to forecast financial feasibility of a model where an existing traditional farm transitions a portion of their land to individual growers, leasing the land and a set of farm related services. I generated a set of outcomes both for the participating individual growers and for the traditional farm that would participate in this multi-farm project. I assessed scenarios for the individual growers referencing published research data on costs and benefits of selling via different market channels. Scenarios for the traditional farm varied in the numbers of acres transitioned, as well as the number of individual vegetable growers to whom they would be leasing. Results showed that the proposed model is economically viable for individual growers when selling via direct market channels but would not be viable for vegetable growers selling via a wholesale market. Results also indicate that the model is marginally financially viable for the traditional landowning farm.
This research may be of interest to traditional land-holding farms, and small growers that are considering alternative approaches to land use and to policy makers. The CBA from this project may also have value as a forecasting tool to new or existing incubators looking to plan financial roadmaps for their own organizations, or for a collective group of individual farmers looking to participate in a joint venture.Extension Studie
Thinking Outside the Black Box: Justifying Beliefs in the Age of Opaque Autonomous AI Systems
No full textIn March 2025, Manus AI emerged as “the world’s first fully autonomous AI agent” that’s capable of end-to-end decision-making without human intervention. Unlike previous AI systems, Manus represents a fundamental shift in AI-Human interactions as the system can independently identify problems, formulate approaches, execute solutions, and, most significantly, adapt its methodologies based on outcomes without human guidance. Yet this rush toward autonomous AI has neglected a fundamental philosophical question that should precede any deployment of self-governing AI systems: What is the epistemic status of beliefs formed based on outputs from AI systems whose operations remain fundamentally opaque to human understanding? When we deploy autonomous AI systems to make decisions, we are effectively authorizing them to form “beliefs” about the world and then act upon those beliefs.This question becomes particularly pressing in light of the “black box” nature of current AI systems. While these systems consistently produce accurate outputs, the processes through which they arrive at these conclusions remain fundamentally opaque. In this paper, I examine the epistemic opacity challenges of AI systems alongside an empirical analysis of current epistemic attitudes everyday AI users hold toward these systems, based on survey data collected on this subject. I will then argue that Goldman's reliabilist theory of justification offers a compelling solution by shifting focus to the reliability of belief-forming processes, which accommodates our counterintuitive acceptance of beliefs formed through processes whose operations remain opaque to us.Computer Scienc
The Weaver of Stories
No full textThe title of this short story collection is The Weaver of Stories. Why, The Weaver
of Stories? The word weaver is not a simple word, it is a rather complex word that
intersects all aspects of life. For many, the word weaver is most commonly associated
with threading material together. For others, the word weaver may be related to the
weaverbird, which is part of the finch family. While for others, the word weaver may
conjure up a variety of thoughts, perceptions, impressions, and ideas, specific to bringing
people together. My stories strive to implement many aspects of this word, by
meticulously layering them together to create a unique and individualized experience.
While my collection of short stories may not overlap, there is a cohesive idea of
overcoming, surviving, and proving personally resilient. The thematic intent of this
collection has been to ballast as many perspectives around survival, implicit and explicit,
and to extrapolate an underlying tone of resilience, persistence, and resistance.
My thesis is a journey through the lives of a variety of characters, both human and
non. It explores their angst, heartache, sorrow, and love. As you voyage through my
stories, you will witness how I have chosen to weave the very fabric of my unique stories
together. The stories are as much about the characters’ lives as they have to do with the
stories themselves.
Ultimately, the ambition of each of these stories has been to drive home a similar
theme of triumph, overcoming and the integrity of the human condition. You’ll be cast
into a rich environment of homesickness, nostalgia, friendship, loss, and love. My stories
intend to explore the very nature of the biopsychosocial makeup of the human
experience, while also examining the intrinsic drive to prove victorious
Masticatory Function and Cognitive Health: Investigating a Molecular Connection Between Oral Tissues and the Brain
No full textBackground: Amongst the rapidly growing elderly population, there is increasing need for comprehensive prosthodontic treatment to address impaired masticatory function of many etiologies3. Compromised oral health may contribute to a myriad of systemic pathologies, including cognitive decline. Several studies have observed an association between poor masticatory function in elderly patients and impaired cognitive function6. Previously, we identified a novel retrograde transport mechanism of exosomal neprilysin, a neuroprotective enzyme, from the masseter muscle to the hippocampus through the trigeminal nerve4.
Aim: This study aims to identify additional neurotrophic agents that are produced in various oral tissues (OT) and retrogradely transported to the brain.
Project-design: This study evaluates: 1) neurotrophic mRNA expression profiles in murine cells from submandibular-glands (SMG), dental-pulp (DP), brain-astrocytes (AST), and neuronal cell line (CATHa) using next-generation-sequencing; 2) protein expression of brain-derived growth factor (BDNF) and nerve growth factor (NGF) in SMG/DP cells versus AST/CATHa-cells via Western blot; and 3) the growth support of CATHa by exosomes isolated from other cells.
Preliminary-results:
- Neurotrophic mRNAs were significantly overexpressed in SMG/DP cells versuss AST/CATHa.
- Western blot analysis of BDNF/NGF protein expression in SMG/DP cells was abundant, but minimal in AST/CATHa.
Conclusion: OT-cells exhibit higher neurotrophic mRNA expression than brain-cells, with corresponding proteins abundant in OT-derived exosomes. This suggests that OTs may play a crucial role in supplying essential biomolecules to the brain. Results from this ongoing study further highlight the impact of masticatory function and oral health on systemic health and provides hope for development of therapeutics.Prosthodontic
You Can't Stay Here Forever
No full textYOU CAN'T STAY HERE FOREVER is a literary mystery set at an elite New England liberal arts college in the age of climate breakdown and societal upheaval. The novel follows Sofia, the campus's part-time Buddhist chaplain, as she navigates spiritual doubt, a side-hustle as a bartender, and a budding romance with Danny, a nonbinary professor who's cagey about his past. When an intense undergrad named Billy disappears from campus after an emotional meeting during office hours, Sofia sets off to find her in a quest involving confusing academic hierarchies, a secret lesbian-separatist botanical farm, and a remote island in Maine--as well as Sofia's own rocky internal landscape. Ultimately, Sofia finds clues to Billy's disappearance when she learns about Danny's childhood in a militant, clandestine leftist milieu redolent of the Weather Underground.Author's Origina
The Role of Muslim Sufi Shayyukh in Formation and Preservation of Religious Identity of Muslims in the United Kingdom
No full textThis study explored the techniques used by three generations of Muslim Sufi Shayyukh [plural of a Muslim religious leader, Shaykh in Arabic], of Eidgah Sharif, Rawalpindi, Pakistan, mainly between 1970’s-2025, to help their British Muslim followers with formation, preservation and transfer of positive religious identity as law abiding citizens of the UK and transforming them into peace builders. Pakistanis from remote areas of Pakistan with low literacy and few professional skills were brought to the UK in the 1950s as cheap labor to keep older textile mills competitive after electrification. Low pay and the need to live near the mills led to Muslim-majority neighborhoods. Subsequent family reunions created a critical need for preserving positive religious identity in the presence of socio-economic, linguistic, cultural, and racial tensions with the host society. Most Pakistanis were unaware of the differences between British and Pakistani cultures before arriving, and they had little religious literacy. The new land contributed to their economic well-being, but they needed guidance to navigate the many differences. Labels of extremism and terrorism presented additional challenges for Muslims. The Shayyukh realized the challenges their followers were facing as early as the 1980s, when they started helping them integrate into British society, while still retaining their positive religious identity and helping them transfer those benefits to the next generation. This ethnographic study used interviews of the Shayyukh’s followers in the UK and Pakistan, observations, field notes, multi-modal data from social media, including photographs, screen shots of videos, and video recordings of publicly-delivered speeches. Discourse analysis and multimodal discourse analysis of data revealed that the Shayyukh used traditional Naqshbandi Sufi Muslim concepts of Sohbat [companionship with the Shaykh] and Rabta [connection(s)] in creating the desired changes. Five specific methods used are given below. First, they developed in their followers a deeper understanding of Islam and its application in domestic, professional and public lives. The second method involved role modeling applied to religion in domestic and public lives. The third method was to offer hope, resilience and training to followers seeking positive alternatives, particularly in the face of adversity. Fourth, they helped their followers understand who they were in their new land, and how to become part of it in the following ways: by being good, caring neighbors even when harshly treated; by getting educated; by getting good employment; by doing charity; among other things; all of which should help dispel negative stereotypes. The fifth method was to engage youth in celebrating their religious identities by organizing and performing in formal religious gatherings. They made extensive use of relationship-building to help their followers. Most important was making the new land their arena by being flexible, without sacrificing Islam’s core religious values, integrating instead of self-segregating, getting to know the local community and building successful relationships with it. To do this, the Shayyukh used the language of hope and positivity, visiting their followers’ homes frequently, never judging them nor closing doors on them.Extension Studie
Out-of-Distribution Generalization in Biological and Artificial Intelligence.
No full textThis past decade has seen unprecedented success in Artificial Intelligence (AI), pushing the frontiers in ways most experts could have never predicted. However, most of this success has come in the form of performing well inside the data distribution the models have been trained with. Out-of-distribution (OOD) generalization still remains the Achilles’ heel of modern AI. In contrast, biological systems exhibit a remarkable ability to adapt to novel situations. This thesis addresses this critical generalization gap, by studying biological and artificial intelligence in tandem. The work presented includes new mathematical frameworks designed to better formalize generalization, behavioral benchmarks to identify the limits of both human and AI generalization capabilities, experiments to identify the underlying mechanisms driving generalization in both brains and neural networks, and engineering solutions to incorporate these findings to improve AI. To this end, this thesis presents scientific contributions made to the fields of Machine Learning, Computer Vision, Computer Graphics, Computational Neuroscience, and Psychophysics. Throughout the thesis, the goal of this work has been to advance our understanding and improve OOD generalization by working at the intersection of biological and artificial intelligence.Engineering and Applied Sciences - Computer Scienc