21090 research outputs found
Sort by
Beyond the Reason-Emotion Divide: Philosophical Theories of Autonomy from a Neuroscience Perspective
No full textThis dissertation explores how recent neuroscience research might bear on philosophical theories of personal autonomy, with a special focus on the work of Christine Korsgaard and Harry Frankfurt. A central question within the personal autonomy literature is what gives our actions self-governing authority? Many approaches have answered this question with requirements about how we ought to reason or feel about our desires. One prominent way of dealing with this question of authority is a requirement of reason being “in control” of our emotions, presenting the relationship between reason and emotion as competitive processes fighting for control over our decisions. However, by examining recent neuroscience research related to the concept of autonomy, I argue that this research paints a different picture, where reason and emotion function cooperatively rather than antagonistically. Furthermore, the research suggests a prominent role for emotion in many different autonomy-related processes.
In order to widely capture the many processes that underlie autonomy, I discuss the neuroscience of decision-making, self-control, voluntary action, and the conscious feelings related to agency and ownership over our actions. I examine some of the emerging trends in cognitive neuroscience that suggests that complex behaviours like decision-making and self-control emerge from large-scale neural networks located across widely distributed areas of the brain, where cognitive, emotional, and motivational information are deeply integrated at the neural level. I argue that this new notion of cognitive, emotional and motivational information as integrated in various processes, such as decision-making and control, has a direct impact on the concept of personal autonomy. First, this integration suggests that cognitive, emotional and motivational information all cooperatively contribute to autonomy processes, rather than competing. Second, the research suggests a larger role for emotion and motivation in processes like decision-making and self-control than is commonly assumed in philosophical theories of autonomy. Therefore, I argue that this integrated neuroscientific perspective highlights some important tensions between the neuroscience research on autonomy processes and philosophical theories of personal autonomy, like those of Korsgaard and Frankfurt.
I examine the possible implications that the neuroscience of decision-making, self-control, voluntary action, and conscious feelings of agency may have on the autonomy theories of Korsgaard and Frankfurt. I point out several key tensions between the neuroscience research and how these theories of autonomy understand desires as motivating our actions, the role of emotion in decision-making and self-control, and whether we ought to rely on our conscious feelings of agency and control over our actions when determining whether our actions are autonomous. I suggest overall that an integrated neuroscientific understanding of the processes that support autonomy-related behaviours can provide a novel approach to understanding the concept of personal autonomy
Analysis and Design of Lens Antennas for Power-Constrained Applications
No full textThe ever-increasing demand for faster data rates as well as overcrowding in the sub-6 GHz spectrum has driven the shift to using higher frequency bands. While the use of higher frequencies can facilitate bandwidth requirements needed to meet the required data rates, they struggle with high Free-Space Path Loss (FSPL) which require specialized solutions to overcome. Phased Array Antennas (PAAs) have attracted immense attention in recent years. PAAs are able to make use of a large quantity of antennas to produce a gain high enough to overcome FSPL while also benefiting from compactness and the ability to rapidly steer and shape the beam. While they have been shown to be effective solutions for many applications, most PAAs depend on a large number of active amplifiers, which entails higher upfront costs, high power consumption, and high thermal dissipation. Such challenges must be addressed for power-constrained or heat-sensitive applications.
This thesis presents a detailed analysis of existing solutions in literature and examines their trade-offs. The Lens Antenna Subarray (LAS) architecture is proposed as a solution, which offers low power consumption while keeping the Gain Over Noise Temperature (G/T) figure of merit for performance competitive with active arrays by leveraging both the directive properties of dielectric lenses as well as the flexibility of traditional PAAs. This thesis focuses on the design of a single lens and its feed network, referred to as a subarray.
To produce a practical example, a Satellite Communication (SATCOM) receiver is chosen as the target application, and a subarray is designed. An ultra-wideband lens with a novel permittivity profile is designed which can provide up to ±64° of -3 dB steering, an improvement over similar Printed Circuit Board (PCB) compatible designs which typically do not provide more than ±50° of steering. Additionally, feed antennas are designed to provide wideband operation over the SATCOM receiving frequency range of 17.7 to 21.2 GHz. A total of 19 antennas are arranged in a novel feeding arrangement which enables the use of circular polarization with the lens, which has not yet been shown in literature.
As the concept of LASs are relatively new to literature, there are many potential directions in which the concept can be developed. Further improvements to the lens, the simplification of feeding antennas, and array-level design are all areas which can be investigated in detail
Towards Secure and Efficient Route Computation for Cross-Chain Message Delivery
No full textDemand for blockchain applications has led to a surge of new public blockchains. However, this fragments liquidity and pushes users to bridge across unfamiliar protocols, increasing risk and complexity. Cross-chain communication enables interoperability, allowing contracts to execute logic and move assets across chains. Yet current delivery solutions either support message passing only between directly connected chains, limiting connectivity, or are centralized and route through a single hub chain that introduces a single point of failure and requires trust in the hub operator.
Inter-blockchain communication can become more robust by leveraging concepts from traditional network architectures, including routing, name resolution, and policy-based message delivery. These mechanisms can increase connectivity by enabling chains that are not directly connected to communicate securely over multi-hop routes.
This thesis studies the problem of policy-driven cross‑chain routing: Current cross-chain routing is largely ad-hoc and manual, and does not reliably respect users' security or cost preferences when no direct connection exists. Given a dynamic inter‑chain topology and user policies (e.g., security thresholds, fee budgets, latency targets), we compute routes over multi‑hop Inter-Blockchain Communication (IBC) while ensuring (a) security constraints are strictly enforced on-chain and (b) preference constraints (e.g., minimizing gas costs) are met with practical guarantees. This is challenging because the required inputs (e.g., fees, validator sets, congestion, and application-specific state) change independently on each chain, yet the resulting route and its policy compliance must be verifiable on the destination chain at a reasonable cost. We present a modular stack: a Transport Layer with Policy Enforcement Module, a Relayer Control Plane for route computation, and a Relayer Data Plane for execution, which separates concerns between policy specification, route computation, and delivery.
We introduce three routing methods: (1) Single‑Relayer routing, which computes routes off‑chain independently by off-chain relayer nodes, (2) zkRouter, which computes routes off‑chain with a succinct zero‑knowledge proof of policy compliance and (3) Relayer Network, a new collaborative overlay that distributes operational load (client updates, packet relaying) across relayers. Our prototypes demonstrate that our stack is practical and achieves higher decentralization, better connectivity, and greater scalability, enabling richer and safer cross-chain applications while preserving IBC’s security assumptions and without significant fee overhead. Our evaluation shows: (1) near 90% connectivity vs. 15% for hub-and-spoke; (2) more than 30% connectivity after removing top four chains, reaching 50% with topology upgrades; (3) less than $0.10 on-chain cost per message; (4) scales to more than 10^6 messages maintaining low processing time
Field-Theoretic Simulations of Binary Blends of Complementary Diblock Copolymers
No full textThe phase behavior of binary blends of AB diblock copolymers of compositions f and 1 − f is examined using field-theoretic simulations. Highly asymmetric compositions (i.e., f ≈ 0) behave like homopolymer blends macrophase separating into coexisting A- and B- rich phases as the segregation is increased, whereas more symmetric diblocks (i.e., f ≈ 0.5) microphase separate into an ordered lamellar phase. In self-consistent field theory, these behaviors are separated by a Lifshitz critical point at f = 0.2113. However, its lower critical dimension is believed to be four, which implies that the Lifshitz point should be destroyed by fluctuations. Consistent with this, it is found to transform into a tricritical point. Furthermore, the highly swollen lamellar phase near the mean-field Lifshitz point disorders into a bicontinuous microemulsion (BμE), consisting of large, interpenetrating A- and B-rich microdomains. A BμE has been previously reported in ternary blends of AB diblock copolymers with its parent A- and B-type homopolymers, but in that system the homopolymers have a tendency to macrophase separate. Our alternative system for creating BμE is free of this macrophase separation
Ancestry Deconvolution via Differential Privacy
No full textThis thesis presents the first study of ancestry determination under differential privacy (DP). Direct-to-consumer genomics companies, such as 23andMe, offer ancestry testing to millions of individuals, yet remain vulnerable to severe data breaches. Such incidents are especially concerning because genomic data is uniquely identifying, highly correlated, and permanent once exposed. At the time of writing, 23andMe disclosed a catastrophic breach in October 2023 that compromised the genetic profiles of an estimated 6.9 million users, underscoring the urgent need for stronger privacy guarantees in genomic analysis.
In this work, we investigate the application of DP to ancestry deconvolution. Using the 1000 Genomes dataset and Gnomix, a state-of-the-art ancestry inference model, we evaluate how privatizing single nucleotide polymorphism (SNP) data affects ancestry classification accuracy. We implement both naïve and correlation-aware local differential privacy (LDP) mechanisms across varying privacy budgets, enabling a systematic study of the privacy-utility trade-off in ancestry inference.
Our results demonstrate that while naïve DP perturbations significantly degrade accuracy, correlation-aware LDP mechanisms preserve substantially more predictive power by accounting for linkage disequilibrium (LD). This thesis establishes a foundation for private ancestry deconvolution, providing an empirical benchmark of state-of-the-art DP methods in genomics and highlighting both the challenges and potential of integrating DP into ancestry testing
The Contributions of ESRP1 to the Functions of the Intestinal Epithelium
No full textEpithelial Splicing Regulatory Proteins 1 and 2 (ESRP1 and ESRP2) are RNA binding proteins expressed exclusively in epithelial cells. They direct a splicing program necessary for maintaining important epithelial cell characteristics, including cell-cell adhesion, anchorage to the basement membrane, and cell-cell communication. ESRP1 and 2 have been studied for their importance in development. The loss of ESRP1 causes a series of craniofacial defects called cleft lip and cleft palate, while the loss of both ESRP1 and ESRP2 result in more severe versions of these defects, several epithelial organ formation defects, and a skin barrier defect which causes significant water loss. ESRP1 is known for its role in craniofacial development, epidermal barrier development, and cancer progression. However, its role in other epithelial organs where it is highly expressed, such as the large intestine, remains understudied. Mice with a hypo-morphic mutation of Esrp1, termed triaka, exhibited decreased intestinal wound healing and increased intestinal permeability. Thus, we hypothesized that ESRP1 contributes to intestinal homeostasis and intestinal barrier integrity by sustaining tight junction localization and intestinal cell proliferation. This thesis project sought to investigate the functions of ESRP1 in maintaining intestinal homeostasis using mouse colon organoids and mouse colon organoid-derived monolayers as a model. Upon the deletion of ESRP1 and subsequent mechanical dissociation of the organoids, we observed a significant decrease in organoid re-formation. Esrp1 KO organoids exhibited no change in organoid cell proliferation. However, they did exhibit a decrease in the expression of Lgr5, an intestinal stem cell marker and receptor for R-spondin. LGR5 helps to maintain the stem cell niche by promoting the Wnt signalling cascade through binding to R-spondin, a Wnt agonist. Thus, its downregulation in Esrp1 KO organoids suggests that ESRP1 helps sustain of the intestinal stem cell niche by maintaining the response of the intestinal epithelium to the Wnt signalling cascade. As R-spondin is produced by subepithelial stromal cells, this would suggest that ESRP1 is necessary for proper epithelial-mesenchymal communication in the intestine. This is ultimately similar to its observed role in craniofacial development. Through investigating the role of ESRP1 in maintaining intestinal barrier integrity, ZO-1 staining showed that tight junctions are still able to assemble properly in Esrp1 KO organoids but become more diffuse in Esrp1 KO monolayers. The loss of ESRP1 resulted in a slight decrease in the barrier integrity of the organoid-derived monolayers. This contrasts with published findings in other cell lines, suggesting that the dependence of epithelial barrier integrity on ESRP1 may vary based on tissue and the chosen model of the epithelium. These findings will provide a solid foundation for further investigations into the role of ESRP1 in maintaining intestinal homeostasis, which will enable future research to uncover its connection to pathological conditions such as Inflammatory Bowel Disease
Robust 4D Millimeter-Wave Radar Perception in Adversarial Environments
No full textThis thesis investigates the robustness of 4D mmWave radar perception for autonomous driving, emphasizing real-time, point-cloud-based object detection in adverse and enclosed environments. Unlike conventional radar studies that rely on range--Doppler or heatmap representations, this work leverages the native 4D radar point cloud as the sole sensing modality. This design enhances compatibility with modern 3D perception architectures, reduces computational overhead, and enables seamless integration within existing autonomous driving stacks.
The study begins with a comprehensive analysis of perception sensing modalities---camera, lidar, and radar---to contextualize their relative strengths, limitations, and degradation mechanisms under visibility-challenged conditions. A system-level characterization of 4D radar measurements is presented, highlighting their unique spatio--temporal properties, the preprocessing pipeline, and the effects of dust, multipath interference, and metallic reflections in operational environments.
Two complementary perception pipelines are developed. The first, a model-driven approach, integrates adaptive noise filtering, unsupervised clustering, and rule-based 3D classification. It demonstrates strong real-time performance in harsh indoor environments but reveals a limitation: the inability to detect fully static pedestrians, inherent to Doppler-reliant sensing. The second, a learning-based framework, adapts lidar-style 3D detectors through a radar pillar feature encoder, enabling effective pretraining on public datasets and fine-tuning on custom indoor scenarios. The fine-tuned model achieves a substantial gain in pedestrian detection accuracy, confirming the advantage of data-driven radar perception.
Together, these results establish a unified and robust framework for standalone 4D mmWave radar perception, illustrating both its feasibility and its remaining challenges toward deployment in safety-critical autonomous and industrial applications
Examining Enabling Conditions of Multi-loop Social Learning in Integrated Flood Risk Management: Evidence from Ontario’s Conservation Authorities in a Flood Management Network Context
No full textFlood risk remains a persistent societal challenge, as no existing measures can offer complete protection against its impacts. Despite advancements in forecasting technologies, infrastructure and policy, flood events continue to result in substantial economic, social and environmental consequences. The complexity of managing flood risk stems from the interaction of dynamic and interrelated factors such as land-use change, risk constructs, governance structures, stakeholder priorities and coordination mechanisms. Integrated Flood Risk Management (IFRM) offers a comprehensive approach, recognizing the need for coordination across governmental levels, sectors and stakeholders while adapting to changing conditions. Within these interconnected socio-ecological systems, continuous learning and adaptation are essential. Social learning, particularly Multi-Loop Social Learning (MLSL), is a key supportive element of IFRM, yet it remains underexplored in this context.
This dissertation investigates the presence of theoretically grounded MLSL capacities and enabling conditions in the practices and collaborative processes of Ontario’s Conservation Authorities (CAs). Particular attention is given to their interactions with the broader Ontario flood management network. These MLSL factors are theorized to play a critical role in supporting the development and application of an IFRM strategy. The IFRM strategy used in this research is modeled after a multi-phased, bio-regionally based, iterative, real-world and documented example: the European Union’s Floods Directive. The study examines how MLSL capacities align with the demands of such an IFRM approach in the Ontario context. Therefore, it situates Ontario within a broader Canadian conversation about the intersection of river basin-based water resources management and MLSL.
Ontario’s CAs, which are river basin–based organizations with legal mandates in flood risk reduction, served as embed units of analysis within the wider case study (i.e., the CA flood management network) for this research. A two-round Delphi survey was conducted with 20 flood risk management (FRM) experts. Survey questions were designed to reflect MLSL factors derived from a previously developed research framework which focused on said factors in the context of Québec’s watershed management organizations. The modified Delphi approach also made it possible to capture both consensus and divergence between academic and practitioner perspectives.
Findings indicate that several enabling MLSL capacities are evident in Ontario’s IFRM setting. These include (1) collaborative partnerships and networks, (2) an intentional approach to learning regarding collaborative processes, (3) sustained participation with governmental stakeholders, (4) cross-sectoral collaboration, (5) internal technical expertise, and (6) learning related to project goals. Respondents attributed these capacities to the CAs’ pivotal roles in flood management networks, long-standing engagement with municipalities and the province, their ecosystem-based approach, and their ability to convene diverse stakeholders across watersheds. CAs were also recognized for their multidisciplinary teams, adaptive management practices, and facilitation skills.
Conversely, the study identified several areas where MLSL capacities are lacking. These include (1) shared data access among governmental actors, (2) collaborative decision-making across governmental levels, (3) an enabling democratic environment, (4) in-depth project reflection using formalized assessments, and (5) access to external expertise. Respondents attributed these gaps to uncertainty about data access, staff and funding constraints, inconsistent capacity among CAs, governance limitations, and unclear roles of external experts. These gaps highlight both institutional and policy limitations that impact the potential to fully transition toward IFRM in Ontario.
This research isolates and analyzes specific MLSL themes, thus, making it possible to assess specific conditions that enable capacity for MLSL. Two key dimensions emerged: (1) the extent to which MLSL capacities are present and (2) how they manifest across IFRM phases. Together, these insights reveal the degree to which MLSL supports IFRM strategy development and implementation. A cross-comparison with a seminal study found convergence on 9 of 11 MLSL themes. This degree of alignment suggests that MLSL capacity challenges are broadly consistent across Canadian river basin-based water resources management contexts; particularly between Ontario and Québec.
This study contributes to scholarly discourse by advancing understanding of MLSL in IFRM settings and offering practical insights for flood management organizations seeking to transition toward more integrated and adaptive approaches. The broader problem this research addressed is the extent to which watershed management agencies, or similar institutions, can effectively transition from one management model to another, particularly when such a transition necessitates the development of specialized MLSL capacities required for implementing the new strategy or model. In parallel, the research highlights policy needs by showing where Ontario’s flood strategy can be reinforced: shared data systems, inclusive decision-making, reflexive evaluation, expanded expertise and sustained multi-sectoral collaboration
A Knowledge Representation for, and an Application to Requirements Elicitation of, Rhetorical Figures of Perfect Lexical Repetition
No full textRhetorical figures, such as rhyme and metaphor, affect human discourse by providing essential semantic and pragmatic information that generate a set of attentional effects such as salience, aesthetic pleasure, and memorability, that enhance the receiver’s attention. Ploke is one kind of rhetorical figure, that of perfect lexical repetition, which is a word or phrase that repeats with the same form and meaning in a passage. Rhetorical figures, including plokes, are largely ignored in natural language processing (NLP) and artificial intelligence (AI). This thesis aims to take two steps towards AIs’ being able to handle plokes as they occur in natural language. It first develops a knowledge representation model of the general concept of Ploke in the form of an ontology that represents the classification of Ploke, the forms of plokes, and the neurocognitive affinities that affect attention. This ontology will help AIs to understand and generate plokes. The ontology proposed in this thesis is able to represent the related knowledge of ploke and its subtypes. The ontology is able also to represent the neurocognitive affinities of ploke and its subtypes by representing their relations to various types of perfect lexical repetition characterized by the positions in which the repetitions occur. After observing that rhetorical figures are used to enhance persuasive discourse, the thesis hypothesizes that a requirements elicitation interview that uses plokes is more effective than one that does not. It then describes a test of this hypothesis in which the interviews were conducted by a simulated AI elicitation bot, which used some plokes in half of its interviews and avoided plokes entirely in the other half of its interviews. The experiment showed that the interview questions and statements conveyed by the simulated AI elicitation bot in its ploke-using requirements elicitation interviews were easier to recognize by the interviewees and were more memorable to them than those in its ploke-avoiding requirements elicitation interviews
Resource Management for Edge-Assisted Extended Reality
No full textExtended reality (XR) enables immersive experiences by seamlessly merging the physical and digital worlds. Supporting such experiences requires real-time and high-quality rendering of virtual content to generate video frames, which is computationally intensive and poses a challenge for resource-constrained XR devices. To overcome this limitation, a promising approach is to offload rendering tasks to nearby edge servers with powerful computing resources. In an edge-assisted XR system, interdependent tasks, including video frame rendering, encoding, and transmission, need to be executed in a pipeline, which consumes substantial communication, computing, and caching resources. The efficiency of network resource provisioning has a direct impact on users' quality of experience (QoE), which reflects the presence and immersive of a user during virtual content viewing and is measured by the weighted sum of visual quality, quality variation, and round-trip latency. Our objective is to efficiently manage multi-dimensional network resources for the XR service to improve user QoE under dynamic network environments. However, the technical challenges are as follows: 1) given the spatiotemporally varying service demand caused by user mobility, how to proactively provision edge resources for the service while achieving satisfactory user QoE; 2) how to adaptively allocate edge resources for individual users to accommodate demand fluctuations caused by dynamic viewing behavior; and 3) in the presence of task dependencies in the pipeline, how to jointly coordinate task processing parameters (e.g., rendering quality, frame encoding type) to improve user QoE.
In this thesis, we design efficient resource management schemes for an edge-assisted XR system to address the above challenges. First, a mobility-aware resource provisioning scheme is proposed to enhance resource utilization while satisfying user QoE on a large timescale. Specifically, we present a mobility model tailored for XR users to capture both user spatial movements and interaction features. Then, we estimate user-specific model parameters and adopt a sample average approximation method to model the relationship between user QoE and the consumption of communication and computing resources. A coordinate descent algorithm is designed to make resource reservation decisions, where a deep neural network provides a valuable initial point to accelerate convergence. Simulation results demonstrate that the proposed resource provisioning scheme is more efficient in reducing network resource consumption while satisfying user QoE, compared with benchmark schemes. Second, we develop an adaptive volumetric video caching and rendering scheme to enhance real-time user QoE by considering dynamic user viewing behaviors. Particularly, volumetric videos of different quality levels need to be cached, rendered, and delivered to XR devices for different viewing distances within a time latency. Given limited resources for the service, we formulate a user QoE maximization problem to jointly optimize volumetric video caching and rendering decisions based on users’ real-time locations and viewing distances. To solve this problem, we first design an online regularization-based optimization algorithm to obtain caching decisions. We then present a low-complexity binary search algorithm to determine optimal rendering quality. Simulation results demonstrate that the proposed scheme achieves higher real-time user QoE in comparison with benchmark schemes. Third, we design a scheme for joint selection of rendering quality and encoding type by considering the interdependency among edge processing tasks to enhance long term user QoE. To cope with network dynamics, the rendering quality of frames can be dynamically adjusted, which in turn triggers an intra-frame encoding and leads to a sudden transmission burst. To capture such task interdependency, we formulate a long-term QoE maximization problem under edge computing and communication resource constraints, which jointly selects the rendering quality and either intra- or inter-frame encoding for each frame. To solve this problem, we theoretically analyze the impact of per-frame decisions on long-term QoE and present an online algorithm for decision-making. Simulation results demonstrate that the proposed joint rendering quality and encoding type selection scheme can further enhance resource utilization and long-term user QoE compared with benchmark schemes.
In summary, we have proposed a mobility-aware resource provision scheme, an adaptive volumetric video caching and rendering scheme, and a task dependency-aware rendering quality and encoding type selection scheme for an edge-assisted XR system. This research should provide useful insights for network operators to deliver immersive XR services at low operational costs