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Glass-Boxing Computing With Electronic Textiles: Teaching And Learning With Notional Machines In An Introductory Computing High School Classroom
No full textDeveloping a conception of the invisible and abstract internal processes that translate computer programs into observable outcomes is essential yet challenging for learners. Notional machines are simplified notions that educators adopt to make transparent or glass-box program dynamics to learners while teaching. In this thesis, I examined teaching and learning with notional machines during a 14-week online introductory electronic textiles unit in a charter high school. Two broad groups of research questions guided this dissertation—one, exploring teaching, and two, examining student learning with notional machines. Research questions on teaching included: (1) What notional machines did the teacher adopt? (2) What forms did the notional machines take in practice? Research questions on student learning included: (3) How did students interact with notional machines during the unit? (4) Did notional machines support students’ development of computing conceptual agency? If so, how? (5) How did students’ conceptions of computing systems shift after learning with notional machines? Multimodal data—online class recordings, student pre- and post-unit interviews, and student-generated artifacts—were qualitatively analyzed to answer the questions posed. Overall, observational data analysis provided one of the first frameworks to capture notional machines in practice. Notional machines belonged to one of the five themes depending on the electronic textiles concept being simplified and differed along the levels of granularity. Also, notional machines took two distinct representational forms—verbal explanations and participatory roleplays. Analysis of student interactions with notional machines highlighted the agentic roles learners took: questioning, adopting, explaining notions, and roleplaying program execution. Further, student pre- and post-unit interviews indicated that students’ conceptions of program dynamics shifted from being simplistic to more advanced in a set of everyday physical computing devices, showing promise for student sense-making of computing devices outside their immediate programming context. Overall, findings from this study point to future research directions to further explore teaching and learning with notional machines and their potential to expand computing learning beyond classroom contexts
Deep Basis Fitting For Depth Completion
Full text linkRecovering depth information from a single image is a challenging task.It is a fundamentally ill-posed problem since there exists an infinite number of scene geometries that could give rise to a given image. However, knowing the depths for a few pixels can significantly constrain the set of solutions. Recovering a plausible depth map from an image with sparse depth measurements is referred to as image-guided depth completion and is the focus of this thesis.
We first developed a novel approach called Deep Basis Fitting (DBF) that builds upon the strengths of modern deep learning techniques and classical optimization algorithms which significantly improves performance.The proposed method replaces the final 1x1 convolutional layer used in most depth completion networks with a least-squares fitting module which computes weights by fitting the implicit depth bases to the given sparse depth measurements. In addition, we show how our method can be naturally extended to a multi-scale formulation for improved self-supervised training.
We then extend DBF for depth completion within a Bayesian evidence framework to provide calibrated per-pixel variance.The DBF approach falls short when the underlying least-squares problem is under-determined, i.e. the number of sparse depths is smaller than the dimension of the basis. By adopting a Bayesian treatment, our Bayesian Deep Basis Fitting (BDBF) approach is able to 1) predict high-quality uncertainty estimates and 2) enable depth completion with very few or even no sparse measurements.
While many depth completion methods rely on an external 3D sensor to produce accurate sparse measurements, it is still possible, albeit much more challenging, to generate dense depth from a single camera.structure-from-motion algorithms, such as visual odometry or visual SLAM, solve for both camera motion and scene structures which can be used for depth completion.
To this end, we developed a visual odometry system named Direct Sparse Odometry Lite (DSOL), which builds upon the original Direct Sparse Odometry (DSO).DSOL adopts several algorithmic and implementation enhancements that speed up computation by an order of magnitude compared to the baseline. We follow the data-oriented design philosophy and layout data contiguously in memory, which improves cache-locality and allows for easy parallelization. The increase in speed allows us to process images at higher frame rates, which in turn provides better results on rapid motions.
Finally, we show that the two systems developed above can be integrated together, where sparse points from the monocular visual odometry can be used for depth completion and the completed depth can in turn be used to initialize odometry keyframes
Anisotropic Carbon Nanomaterials And Liquid Crystals: Interactions, Assembly, And Ordering
No full textUnderstanding mechanical responses of materials under load and other forms of stimulus are crucially important to applications including high performance actuators for soft robotics, lightweight but high strength structural materials, energy absorbers, and energy efficient buildings. With the aim for strong yet predictable/programmable mechanical responses, numerous efforts have been devoted to improving the mechanical properties of materials, through intrinsic fine-tuning of material chemistry and properties, extrinsic geometrical optimizations, and global topological designs. One effective approach is to assemble materials into ordered liquid crystal (LC) phases, in which the properties of individual building blocks can be constructively added up in a controlled manner. LC phases of anisotropic carbon nanomaterials including carbon nanotubes (CNTs) and graphene derivatives are of particular interest due to the excellent physical properties of the nanoscale building blocks. In this Thesis, we focus on the controlled assembly of carbon-based nanomaterials into LC phases, understanding their interactions and ordering, then exploit their applications, including high performance robotic artificial muscles and lightweight high strength load-bearing materials.Specifically, meter-long CNT/liquid crystal elastomer composite filaments are fabricated as light and electrically responsive artificial muscle actuators. Through the molecular interactions between the LC molecules and incorporated nanomaterials, the alignment of LC molecules along the filament long axis is enhanced, the mechanical properties, actuation strength and speed are also boosted. The maximum work capacity of the filament can be comparable to biological muscles, with demonstrated potential integration with different robotic systems. To further improve the performance, especially the response speed of the artificial muscles, strong and ultrafast artificial muscles from reduced graphene oxide (rGO)/conducting polymer composite fibers are created. The wet-spun fibers with well aligned, closely packed rGO sheets can achieve ultra-fast (80 ms) and reversible bending. Yarn actuators based on rGO fiber actuators and non-conductive nylon yarns demonstrate 75 J/kg work capacity and 924 W/kg power density, well exceed those of biological muscles. Further, exploiting the ordering of graphene oxide (GO) lyotropic liquid crystals (LLCs), a new dynamic wrinkling system on freely suspended poly(vinyl alcohol) (PVA) soap films templated by 3D printed wireframes during their liquid-to-solid phase transition is developed and utilized to align GO LLCs on flat and curved surfaces. Via varying the size, thickness, and surface energy of the wireframe, molecular weight, concentration, and thickness of the PVA soap solution, and the environmental conditions, a broad range of pattern are generated. Finally, the GO LLCs alignment guided by wrinkling of PVA soap films are extended to shellular triply periodic minimal surfaces (TPMS) for lightweight and high strength architectured materials. An up to 50 times increase of mass normalized compressive stiffness is observed with less than 20% of weight increase. The better understanding of the interaction, assembly, and ordering of anisotropic carbon nanomaterials – LC systems can potentially pave the way for the next generation of multifunctional smart materials with controllable and programmable intrinsic properties
Resource-Constrained Synchrony: Kuramoto Oscillators Competing For Shared Resources
Full text linkMany systems of biological interest exhibit oscillatory behavior, from the beating of a heart to the firing of neurons to the flashing of fireflies. Further, these oscillatory agents are rarely isolated from one another, and so may interact with one another. In the presence of such interactions, one possible outcome is synchronization of the oscillatory motions. Such synchrony may be observed in the simultaneous flashing of a great many fireflies, or the simultaneous firing of many neurons during an epileptic seizure. A classic model that captures this synchronization is the Kuramoto model. However, the Kuramoto model is a toy model, and thus much work has been directed to extending the model by introducing additional dynamics. In the dissertation, we will present two extensions of the Kuramoto model that make it more appropriate to the study or neural systems. The first extension will add a resource dependence to the Kuramoto dynamics, making the internal dynamics of the oscillators more complex, and thereby introducing novel phases into the Kuramoto phase diagram (Chapter 2). The next extension will allow the oscillators to compete for a shared supply of resources, creating a secondary avenue of communication between the oscillators (Chapter 4). This additional communication pathway will generate correlations in behavior, which may have some relevance for the differences observed between functional and structural connectivity measures in the brain. These two studies serve to elucidate some interesting results on the dynamics of Kuramoto oscillators competing for shared resources, and so serve as my primary contribution to the study of the physics of synchronizable systems. Further, as a scientist-educator, I am also interested in and committed to the education of young physicists, and so I have pursued a separate line of inquiry that studies the learning of students in a cross-disciplinary network-neuroscience course using the tool of concept networks (Chapter 6). We will find that student-drawn concept networks are a useful tool in studying the learning process at a high level, but that more thought needs to be put toward optimizing the collection task in order to bring out the full power of this tool. Collectively, these three studies --- two in the physics of dynamical systems and one in education --- have enabled me to develop in my role as a scientist-educator
From Policy To Practice: The Socioeconomic Context Of Clinicians And Patients In Philadelphia\u27s Public Mental Health System
No full textPublic mental health systems across the U.S. face two competing policy pressures: (1) demands to improve the quality of mental health services by implementing resource-intensive evidence-based interventions (EBIs); and (2) cuts or insufficient increases to public mental health funding. The present research sought to understand the relationship between these conflicting pressures by characterizing the socioeconomic context of patients and clinicians in Philadelphia’s public mental health system. Data from all three studies were collected from the city’s ongoing initiative to increase the delivery of a trauma-informed EBI, trauma-focused cognitive behavioral therapy (TF-CBT). Chapter 1 examined both neighborhood and individual measures of socioeconomic context to characterize the youth seeking treatment in an effectiveness trial of TF-CBT. This study showed that children and adolescents seeking trauma treatment in Philadelphia’s public mental health clinics lived in economically disadvantaged, high‐crime neighborhoods; experienced housing instability; and were predominantly racial/ethnic minorities. Chapter 2 employed qualitative methods from the behavioral insights literature—an interdisciplinary literature examining judgment and decision-making—to understand clinicians’ perspectives implementing TF-CBT in resource-scarce public mental health clinics. Clinicians shared that the decision-making process to use TF-CBT can be complex given patients’ multiple psychosocial stressors and traumas. Clinicians expressed feeling intense, often negative, emotions related to the challenges of helping vulnerable youth; and they felt constrained by the norms and practices at their respective mental health clinics. Chapter 3 surveyed clinicians’ economic and job-related stressors to understand how the experiences of economic precarity and resource scarcity might be related to TF-CBT use at these clinics. Clinicians in public mental health clinics experienced significant economic precarity and job-related stress, which were inversely associated with their TF-CBT use. Collectively, these results suggest that patients and clinicians in Philadelphia’s public mental health system face significant economic strain and stress. Patient and clinician resource scarcity may hinder ongoing efforts to improve the quality of mental health services through EBI implementation efforts. Initiatives to economically and psychologically support patients and the public mental health workforce are needed
Epic Translation: Ranna\u27s Sāhasabhīmavijaya And The Afterlife Of The Mahābhārata In Medieval Karṇāṭa
No full textThis dissertation is a study of the Mahābhārata epic’s reception history in medieval South India. It focuses on an early, but understudied, translation of the epic into Old Kannada, the Sāhasabhīmavijaya (SBhV) of Ranna. This work has typically been read from a historico-political perspective, which casts the poet’s patron, Satyāśraya of the Western Cāḷukyas, onto the role of Bhīma, one of the epic heroes. Thus, the SBhV is usually deemed to be a “double narrative” that maps Satyāśraya and the Cāḷukya dynasty’s claims to power over South India onto the world of the Mahābhārata and its heroes. However, this dissertation departs from such an approach in favor of a more literary orientation, arguing that the SBhV retells the epic through the alternate perspective of Duryodhana, the leader of the Kauravas and the narrative’s traditional anti-hero. To unpack this unique re-interpretation of the Mahābhārata, it draws on a narrative critical method, especially influenced by Mieke Bal and Gérard Genette. Narratology has been useful in providing a vocabulary to think through the specific ways in which the SBhV orders the sequence of the epic’s narrative anew, gives it a different rhythm, and disrupts our expectations through focalization. This dissertation contends that it is possible to trace the SBhV’s literary response to the epic tradition and its immediate social location in medieval Karṇāṭa by concentrating on the minutiae of the narrative and the modes in which it narrates the Mahābhārata. It demonstrates that the epic is filtered through the Kaurava’s interpretive lens. In other words, the SBhV’s telling of the Mahābhārata is framed by Duryodhana’s perspective, his values and biases. These narrative innovations are explored through a series of connected essays that focus on themes like dharma, mourning, and friendship
Towards The Development Of Intrinsically Fluorescent Unnatural Amino Acids For In Vivo Incorporation Into Proteins
No full textAcridonylalanine (Acd) is a fluorescent unnatural amino acid that can site specifically label a protein of interest during protein translation. Acd’s incorporation relies on an evolved tRNA synthetase that specifically charges Acd to its cognate tRNA. As the protein mRNA sequence is being translated by the ribosome and an amber stop codon is present, the tRNA will add Acd to the growing protein chain. This labeling strategy has aided several previous biophysical studies involving fluorescence polarization (FP) and Förster resonance energy transfer (FRET) measurements, but to date was limited to solely E. coli expressed proteins. This thesis work will continue to explore Acd’s use in in vitro measurements from E. coli expressed proteins. Mainly this will focus of using Acd as a FRET and FP probe for studying protein-protein interactions in the context of the SOS antibiotic resistance pathway, and α-synuclein aggregation. In addition, I will present the successful incorporation of Acd into mammalian cells. This collaborative effort resulted in the ability to monitor protein localization using confocal microscopy and through fluorescence lifetime imaging microscopy (FLIM). Furthermore, I will demonstrate our synthetic efforts to red-shift Acd’s emission and our preliminary efforts to incorporate these red-shifted Acd derivatives using a newly evolved synthetase. While our current efforts to incorporate Acd derivatives are unsuccessful, we have a crystal structure of the synthetase and Acd that can guide our future enzyme evolution strategy
The Historic Rise Of The Rules Committee And The Restrictive Special Rule Revolution In The Contemporary U.s. House Of Representatives
No full textIn the U.S. Congress, the House Committee on Rules (also referred to as the “Rules Committee”) has become one of the most exclusive congressional appointments for members to receive. This committee’s ability to shape the legislative pathways for bills before they reach the House floor for votes is arguably its most important role. There are four types of special rules that the Rules Committee assigns: open, modified-open, structured, and closed. The most restrictive types of special rules are structured and closed rules—jointly referred to as restrictive special rules. Restrictive special rules often limit the scope and number of amendments for bills, and the amount of time for floor debate. Since the mid-1990s, the Rules Committee has increasingly used restrictive special rules. For example, four times the number of closed rules were assigned in the 115th Congress (2017-2019) than in the 103rd Congress (1995-1997)—with both Congresses having similar amounts of special rules overall. Even amidst this noticeable trend, few empirical studies use bill-level analyses to assess which bills are more likely to receive restrictive special rules. This dissertation uses an original Special Rules Bills Dataset and Craig Volden’s and Alan Wiseman’s Legislative Effectiveness Dataset to examine the relationship between special rules and bill-level characteristics (e.g., bill issue areas, political ideology, bill sponsors/co-sponsors, and bill fate). Furthermore, this dissertation provides new insight on rising restrictive special rule assignments in the 107th (2001-2003), 110th-116th (2007-2021) Congresses and reports four main findings. First, results show that the Rules Committee is more likely to assign restrictive special rules to bills with majority party sponsors and to bills with fewer out-party cosponsors. Secondly, although appropriations bills often receive special rules, they are more likely to receive open/modified-open rules. Third, subcommittee chairmen are more likely to sponsor special rules bills. Finally, results show that restrictive special rules are more beneficial to bills addressing social welfare or civil rights than they are to bills addressing defense or public lands
Remote Force Guided Assembly Of Complex Orthopaedic Tissues
No full textArticular cartilage is a complex orthopaedic tissue with zonal differences in cellularity, cell phenotype, mineralization, and extracellular matrix components. The unique arrangement of these building blocks allows the tissue to withstand cyclic compressive loads over a lifetime of use. In the case of injury or disease, the complex structure of articular cartilage is disrupted, reducing the tissue’s load bearing capacity. To restore the complex structure, and ultimately the function of cartilage, this work establishes and validates both in vitro and in vivo tissue assembly methods utilizing remote fields (i.e., magnetic fields and gravity). First, chondrogenic culture conditions were optimized to promote uniform cartilage growth in vitro. Thereafter, native-like cell gradients were assembled in engineered cartilage tissues using a novel magneto-patterning approach. To do this, a paramagnetic contrast agent, hydrogel precursor solution, and naturally diamagnetic cells were combined above a permanent magnet. After brief exposure to a magnetic field, the cell-laden solution was crosslinked, forming a three-dimensional tissue. The fabricated magneto-patterned tissues were viable, metabolically active over time in culture, and contained depth dependent extracellular matrix, reflecting their cellularity. Moving forward, we established a new nanofracture-based surgical model and advanced cryohistology methods to then assess the therapeutic potential of a novel in vivo tissue assembly method. Previous work in the lab found that awl-based microfracture leads to significant resorption of the subchondral bone, limiting the success of the overlying repair cartilage. To better preserve the osteochondral interface – or the connection between cartilage and bone after marrow stimulation, we designed microcapsules to carry and localize pro-osteogenic agents to the bottom surface of a debrided cartilage defect. Due to their thick shells, these microcapsules sank within a saline carrier solution, enabling gravity-based patterning to the osteochondral interface. We discovered that the therapeutic microcapsules best preserved bone nearest the nanofracture holes, and across the osteochondral interface, as assessed 1- and 2-weeks post-surgery via fluorochrome labeling and alkaline phosphatase staining. Together, these studies push the field of biofabrication forward to consider noncontact methodologies of tissue patterning, and the clinical translation of such approaches
Functional Brain Network Development: Shifting Boundaries & Environmental Influences
No full textIn this work, we take a network science approach to studying large-scale intrinsic brain networks during three important periods of development. In the first study, we employ sophisticated acquisition and analysis tools to investigate functional network development in children between the ages of 4 and 10 (n = 92). We demonstrate that age is positively associated with network segregation at multiple spatial scales, and that associations between age and functional connectivity are most pronounced in visual and medial prefrontal cortex, at two ends of a gradient from perceptual, externally-oriented cortex to abstract, internally-oriented cortex. In the second study, we uncover the community structure of cortex in children aged 9 to 11 years (n = 670). We show that children have similar community structure to adults in early-developing sensory and motor communities, but differences emerge in association areas. Children have more cortical territory in the limbic community, which is involved in emotion processing, than adults. Regions in association cortex interact more flexibly across communities, perhaps reflecting cortical boundaries that are not yet solidified, and uncertainty is highest for cingulo-opercular areas involved in flexible deployment of cognitive control. In the third study, we map the associations between neighborhood SES and functional brain networks in a sample of children between the ages of 8 and 22 years (n = 1012). We characterize network topology using a local measure of network segregation known as the clustering coefficient and find that it accounts for a greater degree of SES-associated variance than mesoscale segregation captured by modularity. High- SES youth show stronger positive associations between age and segregation than low-SES youth, and this effect was most pronounced for regions in the limbic, somatomotor, and ventral attention systems. Collectively, our results provide new insights into how changes in cortical organization give rise to changes in the mind as children grow up, and how variation in the neighborhood environment might in turn affect brain development