VictorJuiz/thesis huggingface dataset:
Microscopic Model-based Reinforcement Learning Approaches for Traffic Signal Control and Coordination
Keywords: model based
model free
reinforcement learning
traffic signal control

Many recent advances in traffic signal control have been proposed using reinforcement learning, most of which undertake the model-free approach. Model-free methods of learning the value function under a specified dynamics, can often fail to differentiate the value updates from the underlying dynamics, making these methods require substantial experiential data to achieve generalization. In contrast, approaches that optimize agent actions w.r.t. a learned dynamics model inherently avoid this tight coupling of dynamics and value, allowing for much faster adaptation as traffic demand profiles change. For this work on single intersection control, we specifically adopt this latter model-based approach that learns microscopic traffic dynamics models and then applies tree-search techniques to optimize control actions. We include performance and generalization evaluation alongside analysis of these methods, and demonstrate that our approach generalizes to a diverse set of traffic demands whereas the model-free approach performs suboptimally in conditions unseen during training.
Characterizing Spatiotemporal Trends of Meteorological Extremes in Southern Ontario, Canada
Keywords: Bayesian inference
climate change
extreme value theory
meteorological extremes
teleconnections

Extreme meteorological conditions are changing in the twentieth century, with potentially severe environmental implications. We explored the changes in these extreme conditions and found seasonal signatures in extreme monthly values of air and dew point temperature and low relative humidity, spatial signatures in those of wind speed and high relative humidity, and no distinct patterns in those of precipitation from 1950–2020 in southern Ontario using the Mann–Kendall trend test and Theil–Sen estimator. We found a minimal role of four large-scale phenomena—Arctic Oscillation, North Atlantic Oscillation, El Niño–Southern Oscillation, and Pacific Decadal Oscillation—in the temporal trends using Bayesian Generalized Extreme Value and Poisson regression models. Warming temperature extremes in the colder months imply changes in ecological phenology. Our findings also suggest that there are fewer extreme cold conditions in colder months and more intense plant water loss and thermal discomfort in warmer months.
Design of Decentralized Robot Architecture and Sim-to-real Transfer Pipeline For Deep Reinforcement Learning Based Rough Terrain Navigation
Keywords: Autonomous Agents
Deep Learning for Robotics and Automation
Search and Rescue Robots

Robots that autonomously navigate real-world 3D cluttered environments need to safely traverse terrain with abrupt changes in surface normal and elevation. This thesis presents a decentralized robot architecture for navigation and a novel sim-to-real pipeline for learning real-world navigation in simulation using deep reinforcement learning. The decentralized robot architecture design avoids the requirement of a powerful central server and improves the robustness of the processes against hardware and software failures by isolating the computing units. A set of experiments were conducted to demonstrate the robustness of the architecture's mapping system against challenging environmental conditions. The sim-to-real pipeline uses deep reinforcement learning to learn a navigation policy from data collected in simulation. It incorporated a combination of sim-to-real strategies to address the reality gap that uniquely exists for 3D navigation problems. A set of real-world experiments demonstrated that the pipeline successfully transferred the learned navigation policy into the real world.
Risk-aware Financial Portfolio Management with Distributional Deep Deterministic Policy Gradient
Keywords: DDPG
Distributional RL
Portfolio Management
Reinforcement Learning
Risk Management

In this research, we will explore a risk-aware distributional Deep Deterministic Policy Gradient (DDPG) based reinforcement learning algorithm to tackle the classic financial portfolio management problem. The objective of our research is to discover a general approach for real-world financial portfolio management that incorporates continuous state and action space, controllable portfolio diversification and investment risk-awareness. The state-of-art distributional reinforcement learning algorithm is employed in our research along with the DDPG algorithm. We chose the Convolutional Neural Network (CNN) as the feature extractor and studied the effect of explicitly introducing the correlation matrix to the feature extraction process. We managed to control the diversification level of the portfolio by applying temperature control over Softmax and produced a risk-averse reinforcement learning agent through a utility-based risk control practice. Extensive experimental results show that our approach leads to a diversified and risk-averse investment strategy that outperforms an equal-weighted strategy consistently and demonstrates great generalizability.
Differential Toxicity and Chemical Composition of PM2.5 Emissions from Varying Wood Burning Conditions
Keywords: biomass
burning conditions
indoor air quality
PAHs
toxicology
woodsmoke

The contribution of residential wood burning to ambient fine particulate matter (PM2.5) has been reported to be as high as 42% in North American urban areas during the heating season. Wood burning PM2.5 can be carcinogenic and cause oxidative stress via generation of reactive oxygen species (known as oxidative potential, OP). This study analyzed the toxicity of PM2.5 from wood combustion and pyrolysis by varying fuel, ventilation, and oxygen availability. The wood was burned in a tube furnace coupled with online CO/CO2 measurements, followed by PM2.5 offline organic carbon/elemental carbon and gas chromatography-mass spectrometry analyses. The toxicity was measured via the dithiothreitol assay (OPDTT) and the benzo[a]pyrene equivalent (BaPeq). Oxygen deprivation increased the OPDTT and BaPeq for most species. Enhanced ventilation conditions caused the highest OPDTT and lowest BaPeq values. Negative correlations were found between OPDTT and some phenolic compounds. The results presented inform best practices to limit health impacts. La contribución de la combustión de madera residencial a las partículas suspendidas menores a 2.5 micras (PM2.5) se ha reportado tan alta como 42% en áreas urbanas de América del Norte, especialmente durante la temporada de frío. La quema de madera emite PM2.5 que puede causar efectos nocivos a la salud como carcinogenicidad o estrés oxidativo a través de la generación de especies reactivas de oxígeno (lo que se conoce como potencial oxidativo, OP). Este estudio analizó la toxicidad de PM2.5 de la combustión y pirólisis de madera variando el combustible, la ventilación y la disponibilidad de oxígeno. La madera se quemó en un horno tubular con mediciones de CO / CO2 en tiempo real, seguido del análisis de las partículas por medio de espectrometría de masas, cromatografía de gases y carbono orgánico / elemental. La toxicidad se midió mediante el ensayo de ditiotreitol (OPDTT) y el equivalente de benzopireno (BaPeq). Se encontró que la deficiencia de oxígeno aumenta el OPDTT para todas las especies de madera y el BaPeq para la mayoría de las especies. La ventilación en exceso dio como resultado el OPDTT más alto y el BaPeq más bajo. Se encontraron correlaciones negativas moderadas y fuertes entre el OPDTT y algunos compuestos fenólicos. Los resultados presentados informan mejores prácticas de quema de madera con objetivo de limitar los impactos sobre la salud.
Transrectal Ultrasound-guided Brachytherapy in Early Cervical Cancers – Utopia or an Upcoming Reality?
Keywords: Brachytherapy
Cervical Cancer
Ultrasound

Rationale: Transrectal ultrasound (TRUS) allows for great soft tissue discrimination in the pelvis. This study investigates the role of TRUS in cervical cancer brachytherapy. Methods: FIGO IB cervical cancer patients treated with MR-guided intracavitary brachytherapy boost were enrolled in this study. TRUS images acquired in axial axis were used to define the High-risk Clinical Target Volume (CTVHR) and pelvic organs at risk (OARs) with and without the applicator in place and compared to contours performed in MR images. Results: CTVHR contours were smaller and thinner in TRUS images. No statistical difference was seen in CTVHR height or width. CTVHR and OARs inter-rater contouring agreement were similar between contours in MR and TRUS when applicators were not in situ. Significant image deterioration was noticed in TRUS images once the applicators were inserted. Conclusions: TRUS images allow for consistent CTVHR and OAR contours whenever brachytherapy applicators are not in place.
GAIA: An “eEcosystem” of Aggregated Information for Plant Biology
Keywords: computational biology
data mining
data visualization
natural language processing
nlp
web development

Currently, there is a plethora of scientific data available online through many different tools, databases, repositories, and websites, making it difficult for researchers to find the information for their prospective investigation. When the Multinational Arabidopsis Steering Committee (MASC) conducted a 2018 bioinformatics survey, they found that researchers have expressed a strong desire to have data centralized. Inspired by this request, the General Agricultural Intelligent Agent (GAIA) was created. Aggregating data for plant biology across many scientific resources, GAIA can create an executive summary that can either summarize a gene or gene product of interest or use Natural Language Processing (NLP) and machine reading (MR) to answer an inputted question. Built with modular design philosophy, GAIA is created with simplicity in mind for its code, allowing new developers to easily access, modify, and create new components for its UI and functionality. GAIA is currently available at https://bar.utoronto.ca/gaia.
The Roles of TrkA and p75NTR NGF Receptors in Corneal Wound Healing
Keywords: Corneal nerves
Corneal wound healing
Neurotrophik Keratopathy
NGF
p75NTR
TrkA

The cornea, i.e., one of the most densely innervated structures in the body, is the window through which we see the world. The loss of corneal innervation through injury, diabetes, tumors, infections and even improper contact lens use leads to neurotrophic keratopathy (NK). NK is a degenerative corneal disease characterized by corneal epithelial breakdown, scarring, and permanent vision loss. The only non-invasive treatment option for NK is recombinant human nerve growth factor (rhNGF); however, the short half-life of exogenous neurotrophin-based therapies limits their efficacy. Accordingly, small molecule ligands have been developed for neurotrophin receptors with more favorable pharmacokinetics and stability, showing promising results for the treatment of several neurodegenerative conditions in recent years. In this study, we investigate the molecular mechanism of NK and the role of the NGF receptors Tropomyosin Receptor Kinase A (TrkA) and p75NTR (neurotrophin receptor) in corneal healing.
Optimization of Compiler-generated OpenCL CNN Kernels and Runtime for FPGAs
Keywords: Compiler
Convolutional neural networks
Deep learning
FPGA

This work explores the viability of end-to-end convolutional neural network inference using OpenCL HLS kernels generated from TVM on Intel FPGAs. We explore layer-pipelined execution for small networks and time-multiplexed kernels for larger CNNs. Naively generated kernels do not produce efficient hardware. We propose a set of optimizations to increase parallelism, resource utilization, and more efficiently use memory bandwidth. They include loop unrolling, tiling, fusion, invariant code motion, cached writes, CL channels, autorun kernels, concurrent execution, and parameterized kernels. These optimizations improve performance up to a factor of 1150x over the naive baseline implementation generated by TVM. Compared to Keras/Tensorflow on a 56-core Xeon 8280, we observe performance improvements up to 4.57x and 1.4x over LeNet and MobileNet but has a slowdown at 0.43x for ResNet-18/34.
Design and Evaluation of a Researcher Platform to Automate Development of Customized Cognitive Games
Keywords: Human Factors Engineering
Interaction Design
Product Management
Usability Evaluation

The Interactive Media Lab (IML) at the University of Toronto has developed a suite of serious games that can be used to administer psychological tasks. To move these games from laboratory to practical use, it was important to establish their scientific validity through validation studies with large samples. With an increasing demand for customized versions for various research studies, a development capacity bottleneck was reached. To find a long-term solution to this challenge, we create a researcher platform, enabling researchers to customize cognitive games for their research studies without the need for coding skills. This thesis describes the human-centred design process of the platform, which culminated in a ready-to-implement prototype, as well as the insights learned from two rounds of usability evaluation. As a novel experiment builder of serious games for psychological tasks, this platform provides insights on leveraging software tools to make online experimentation more efficient.
Design of an Affibody-modified Hydrogel for the Controlled Release of FGF2
Keywords: Affibody
Affinity interactions
Fibroblast growth factor 2
Hyaluronan
Hydrogel
Protein delivery

Proteins have demonstrated immense potential as therapeutic agents in the treatment of ocular diseases as they possess high specificity and affinity for their targets; however, their short half-lives comprise a significant barrier to their clinical translation. To tackle this challenge, affinity-based hydrogels have been employed to tune protein release and prolong therapeutic residence time. In this thesis, we designed an affibody-modified hyaluronan (HA)-based hydrogel as a delivery vehicle for fibroblast growth factor 2 (FGF2). We identified a highly specific affibody binding partner with an intermediate affinity (3.8 x 10-8 M) for FGF2 using yeast surface display. Importantly, we demonstrated controlled release of FGF2 from the hydrogel vehicle by modulating the affibody to protein ratio and showed a stabilizing effect of the affibody on FGF2. Ultimately, this system serves as a versatile delivery platform amenable to any protein with the potential for distinct multi-species release profiles based on specific affinity interactions.
Identifying Barriers and Facilitators to the use of Renal Tumor Biopsy (RTB) in the Management of Small Renal Masses (SRMs) in Ontario and Potential Implementation Strategies to Promote its Widespread Adoption
Keywords: Implementation Science
Knowledge Translation
Modified Delphi Process
Qualitative Research
Renal Tumor Biopsy
Semi-structured Interview

Background: Renal tumor biopsy (RTB) has been shown to be a useful diagnostic tool, however, it remains underutilized in Canada. No previous study has explored the barriers/facilitators of RTB using interviews. Methods: Phase one comprised qualitative telephone interviews with Ontario Urologists to determine barriers/facilitators to the use of RTB in practice. Phase two entailed a modified Delphi process with a panel of experts to determine the most critical barriers/facilitators to address for ongoing work. Results: Four barriers/facilitators were determined to be the most appropriate to address for ongoing work to promote the adoption of RTB in Ontario and included: 1) need for disseminating current Canadian Urological Association guidelines; 2) need for new Canadian guidelines for RTB; 3) Radiologists’ RTB technical skill; 4) Colleagues promoting RTB. Conclusion: This study provides Ontario healthcare with a tool that identified the most appropriate barriers/facilitators, along with interventions, to promote the adoption of RTB.
A Star Put It There: Theatre, Astronomy, and Finding Our Place
Keywords: Astronomy
Existentialism
Philosophy
Science History
Theatre

Western theatre has reflected millennia of our evolving awareness of humanity’s place in the universe, and the more we learn, the smaller—and less significant—we appear. Two case studies illustrate this interplay between theatrical expression and scientific inquiry through close reading and astronomical context. Michael Frayn’s Copenhagen grapples with ethical questions surrounding the work of influential quantum physicists Niels Bohr and Werner Heisenberg, while Maureen Hunter’s Transit of Venus provides a biographical exploration of 18th-century astronomy and personal fallout in the life of stargazer Guillaume Le Gentil. A companion piece to the project, a solo play titled A Star Put It There, offers an increasingly macroscopic contemplation of the cosmos and its existential implications. As science continues to seek new truths about our reality, a renewed humanism prompts celebration of the meanings we create for ourselves and each other, an outlook well-suited to the interpersonal immediacy of theatre.
Patients' Experiences and Perceptions of the Symptom Screening with Targeted Early Palliative Care (STEP) Process
Keywords: cancer
palliative care
qualitative
symptom screening

Symptom screening with Targeted Early Palliative care (STEP) is a novel intervention offering early palliative care to symptomatic patients with advanced cancer. A qualitative descriptive approach was taken to explore patients’ experiences and perceptions of the STEP process and to identify factors considered when deciding to accept or decline a palliative care clinic (PCC) referral. Fifteen patients completed interviews. Participants stated they believed symptom screening helped healthcare providers, but emphasized the additional importance of discussing their symptom scores. Some participants felt shocked/discouraged, and others comforted/supported at being offered a PCC referral; benefits of receiving a referral were noted. Common factors considered when deciding to accept or decline a referral were: perceived symptom burden, perceived need for additional support, and readiness to contemplate a terminal prognosis. This information is important to guide future implementations of STEP and to ensure that timely palliative care is provided to those in greatest need.
Universal Rate-Distortion-Perception Representations for Lossy Compression and Applications using Generative Models
Keywords: Deep Learning
Image Compression
Information Theory
Lossy Compression

In the context of lossy compression, Blau \ Michaeli \cite{blau2019rethinking} adopt a mathematical notion of perceptual quality and define the information rate-distortion-perception (RDP) function. We consider the notion of universal representations in which one fixes an encoder and varies the decoder to achieve a collection of distortion-perception constraints. We prove that the corresponding information-theoretic universal RDP function is operationally achievable in an approximate sense. Under MSE distortion, we show that the entire distortion-perception tradeoff for Gaussian sources is achieved by a single encoder asymptotically. We then characterize the achievable distortion-perception region for a fixed representation for arbitrary distributions. This motivates the study of practical constructions that are approximately universal, alleviating the need to design an encoder for each objective. We provide experiments on MNIST/SVHN suggesting that for image compression, the operational tradeoffs achieved by fixed-encoder machine learning models are comparable to their variable encoder counterparts.
New Perspectives on The Role of Lipid Anchoring on G-Protein Coupling and Activation in the Adenosine A2A-Gs Complex
Keywords: Biochemistry
Conformational Selection
G protein
GPCR
Lipidation
Receptor

The G protein in a G protein coupled-receptor (GPCR) plays an important role in activation of the coupled receptor via nucleotide exchange and release of its α-subunit (Gα) and βγ-subunits (Gβγ) to allow for downstream signaling. We use 1D 19F nuclear magnetic resonance (19F NMR) and 2D heteronuclear single quantum coherence spectroscopy (HSQC) to resolve the conformational states of the α-subunit of the stimulatory G protein (Gαs). Specific sites on Gαs provide an improved perspective on the allosteric response upon 19F-tagging. Gαs exhibits at least two distinct broad conformations; a coupled and uncoupled state, whose populations are dependent on the interplay between receptor, Gβγ, and nucleotide. Gαs can also be modified to be N-myristoylated, which is envisioned to create an enhancement of its association with receptor, membrane, Gβγ, and its function. Here, Gαs is N-myristoylated through an in vitro reaction with the enzyme, N-myristoyltransferase (NMT1) to attach a lipid anchor.
Effects of Hyperoxia Exposure on the Polarization of Alveolar-like Macrophages in a Mouse Model of LPS-induced Lung Inflammation
Keywords: acute respiratory distress syndrome
alveolar like macrophage
alveolar macrophage
ARDS
inflammation
lung disease

Acute respiratory distress syndrome (ARDS) is a severe lung disease of high mortality (30-50%). Patients require lifesaving oxygen therapy, however hyperoxia can induce pulmonary inflammation and cellular damage. Although alveolar macrophages (AMs) are essential for lung immune homeostasis, they become compromised during inflammatory lung injury. Stem cell-derived alveolar-like macrophages (ALMs) are a prospective therapeutic for lung diseases like ARDS. Using in vitro and in vivo methods, I investigated the impacts of hyperoxia on ALMs during acute inflammation. ALMs retain their viability and antimicrobial abilities during moderate hyperoxia exposure in vitro. ALMs possess a hybrid M1/M2 phenotype that remained stable upon moderate hyperoxia exposure. After intratracheal delivery to mice, ALMs adopt further characteristics of primary AMs. Hyperoxia did not exacerbate phenotypic changes of ALMs caused by LPS-induced lung inflammation in mice. These results support the concept that ALMs may have potential benefit in human ARDS patients receiving conservative oxygen therapy.
Breast Cancer Patients’ Experiences While Transitioning to a Virtual Cardiovascular Rehabilitation Program during a Pandemic (COVID-19)
Keywords: Cancer rehabilitation
COVID-19
Exercise
Neoplasm
Qualitative research
Survivorship

COVID-19 restricted access to onsite Cardiovascular Rehabilitation (CR) programs for patients with breast cancer (BC). Objectives: [1] compare BC survivors’ Physical Activity (PA) in onsite CR to virtual CR following the COVID-19 transition and compare to a matched cohort from prior to the pandemic (2018/2019). [2] explore experiences of transitioning to and engaging in virtual CR. Methodology: Quantitative analysis of CR PA data from pandemic (n=18) and 2018/2019 cohorts (n=18), and semi-structured focus group interviews with the pandemic cohort (n=9) in the context of the PRECEDE-PROCEED model. Results: No significant differences in mean but increased variation of activity duration, frequency, and MET-minutes were observed. Focus groups revealed women valued their CR experiences pre-COVID-19 and were anxious during the transition. BC patients recommended virtual programs increase comradery, technology, and professional guidance. Conclusion: PA experiences with a pandemic transition vary among BC survivors. Targeting individualized strategies and exercise prescriptions are important.