arXiv subjects

Thomas J. Rademaker

Publications and source records attributed to Thomas J. Rademaker.

3 recordsLinked to original sources

arXiv2021

Content Sequencing and its Impact on Student Learning in Electromagnetism: Theory and Experiment

We investigate the impact of content sequencing on student learning outcomes in a first-year university electromagnetism course. Using a custom-built online system, the McGill Learning Platform (McLEAP), we test student problem-solving performance as a function of the sequence in which the students are presented aspects of new material. New material was divided into the three categories of conceptual, theoretical and example-based content. Here, we present findings from a two-year study with over 1000 students participating. We find that content sequencing has a significant impact on learning outcomes in our study: students presented with conceptual content first perform significantly better on our assessment than those presented with theoretical content. To explain these results, we propose the Content Cube as an extension to the the mental model frameworks. Additionally, we find that instructors' preferences for content sequencing differ significantly from that of students. We discuss how this information can be used to improve course instruction and student learning, and motivate future work building upon our presented results to study the impact of additional factors on student performance.

physics.ed-ph↗

arXiv2019

Attack and defence in cellular decision-making: lessons from machine learning

Machine learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signalling, like in early immune recognition. We draw a formal analogy between neural networks used in machine learning and models of cellular decision-making (adaptive proofreading). We apply attacks from machine learning to simple decision-making models, and show explicitly the correspondence to antagonism by weakly bound ligands. Such antagonism is absent in more nonlinear models, which inspired us to implement a biomimetic defence in neural networks filtering out adversarial perturbations. We then apply a gradient-descent approach from machine learning to different cellular decision-making models, and we reveal the existence of two regimes characterized by the presence or absence of a critical point for the gradient. This critical point causes the strongest antagonists to lie close to the decision boundary. This is validated in the loss landscapes of robust neural networks and cellular decision-making models, and observed experimentally for immune cells. For both regimes, we explain how associated defence mechanisms shape the geometry of the loss landscape, and why different adversarial attacks are effective in different regimes. Our work connects evolved cellular decision-making to machine learning, and motivates the design of a general theory of adversarial perturbations, both for in vivo and in silico systems.

physics.bio-ph↗

arXiv2017

Untangling the hairball: fitness based asymptotic reduction of biological networks

Complex mathematical models of interaction networks are routinely used for prediction in systems biology. However, it is difficult to reconcile network complexities with a formal understanding of their behavior. Here, we propose a simple procedure (called $\bar φ$) to reduce biological models to functional submodules, using statistical mechanics of complex systems combined with a fitness-based approach inspired by $\textit{in silico}$ evolution. $\bar φ$ works by putting parameters or combination of parameters to some asymptotic limit, while keeping (or slightly improving) the model performance, and requires parameter symmetry breaking for more complex models. We illustrate $\bar φ$ on biochemical adaptation and on different models of immune recognition by T cells. An intractable model of immune recognition with close to a hundred individual transition rates is reduced to a simple two-parameter model. $\bar φ$ extracts three different mechanisms for early immune recognition, and automatically discovers similar functional modules in different models of the same process, allowing for model classification and comparison. Our procedure can be applied to biological networks based on rate equations using a fitness function that quantifies phenotypic performance.

physics.bio-ph↗