Amputation Dream

pages 41-4

Am I Doing This Right? The Emotional Labor of Confronting Inequitable Writing Assessment

During the pandemic, we, like many others, found ourselves reimagining the practices we engage in to best meet the needs of our students. While adjusting to a new class structure was challenging, we found that writing assessment was particularly fraught. To create the most equitable assessment practices, we implemented Inoue’s conception of labor-based grading. Inoue (2019) argues, “A grading contract based only on labor is better for all students and undermines the racist and White Supremacist grading systems we all live with at all levels of education” (p.16-17). These circumstances motivated us to employ labor-based grading given the difficulties many of our students were experiencing as a result of the changed learning environment, as well as the social, economic, and health implications resulting from the pandemic. As one might expect, there was substantial emotional labor that accompanied letting go of old values and assessment practices. Newman, et al. (2009) ask, “How do emotional labor and artful affect translate into our understanding of leadership?” (p. 6). This is an instructive question for many reasons. For one, many writing teachers don’t often think of themselves as “leaders” per se, especially those of us who value collaborative learning and are averse to the banking concept of education. That said, the decisions about assessment are ours to make. While we feel our students benefited from the practices we employed, actually assessing work in this way was often uncomfortable and left us wondering, “Am I doing this right?” This article will address the tensions we experienced and how to better navigate them moving forward. More importantly, we will discuss the ways in which this has allowed us to engage in the necessary but vulnerable work of reflecting on our own internalized hegemonic value systems and how these systems have inadvertently influenced our assessment strategies

Stable and robust neural network controllers

Neural networks are expressive function approimators that can be employed for state estimation in control problems. However, control systems with machine learning in the loop often lack stability proofs and performance guarantees, which are crucial for safety-critical applications. In this work, a feedback controller using a feedforward neural network of arbitrary size to estimate unknown dynamics is suggested. The controller is designed for solving a general trajectory tracking problem for a broad class of two-dimensional nonlinear systems. The controller is proven to stabilize the closed-loop system, such that it is input-to-state and finite-gain Lp-stable from the neural network estimation error to the tracking error. Furthermore, the controller is proven to make the tracking error globally and exponentially converge to a ball centered at the origin. When the neural network estimate is updated discretely, or the state measurements are affected by bounded noise, the convergence bound is shown to be dependent on the Lipschitz constant of the neural network estimator. In light of this, we demonstrate how regularization techniques can be beneficial when utilizing deep learning in control. Experiments on simulated data confirm the theoretical results.acceptedVersio

A comparison of general and ambulance specific stressors: predictors of job satisfaction and health problems in a nationwide one-year follow-up study of Norwegian ambulance personnel

Objectives To address the relative importance of general job-related stressors, ambulance specific stressors and individual characteristics in relation to job satisfaction and health complaints (emotional exhaustion, psychological distress and musculoskeletal pain) among ambulance personnel. Materials and methods A nationwide prospective questionnaire survey of ambulance personnel in operational duty at two time points (n = 1180 at baseline, T1 and n = 298 at one-year follow up, T2). The questionnaires included the Maslach Burnout Inventory, The Job Satisfaction Scale, Hopkins Symptom Checklist (SCL-10), Job Stress Survey, the Norwegian Ambulance Stress Survey and the Basic Character Inventory. Results Overall, 42 out of the possible 56 correlations between job stressors at T1 and job satisfaction and health complaints at T2 were statistically significant. Lower job satisfaction at T2 was predicted by frequency of lack of leader support and severity of challenging job tasks. Emotional exhaustion at T2 was predicted by neuroticism, frequency of lack of support from leader, time pressure, and physical demands. Adjusted for T1 levels, emotional exhaustion was predicted by neuroticism (beta = 0.15, p < .05) and time pressure (beta = 0.14, p < 0.01). Psychological distress at T2 was predicted by neuroticism and lack of co-worker support. Adjusted for T1 levels, psychological distress was predicted by neuroticism (beta = 0.12, p < .05). Musculoskeletal pain at T2 was predicted by, higher age, neuroticism, lack of co-worker support and severity of physical demands. Adjusted for T1 levels, musculoskeletal pain was predicted neuroticism, and severity of physical demands (beta = 0.12, p < .05). Conclusions Low job satisfaction at T2 was predicted by general work-related stressors, whereas health complaints at T2 were predicted by both general work-related stressors and ambulance specific stressors. The personality variable neuroticism predicted increased complaints across all health outcomes

Port-Hamiltonian Neural Networks with State-Dependent Ports

Hybrid machine learning based on Hamiltonian formulations has recently been successfully demonstrated for simple mechanical systems, both energy conserving and not energy conserving. We show that port-Hamiltonian neural network models can be used to learn external forces acting on a system. We argue that this property is particularly useful when the external forces are state dependent, in which case it is the port-Hamiltonian structure that facilitates the separation of internal and external forces. Numerical results are provided for a forced and damped mass-spring system and a tank system of higher complexity, and a symmetric fourth-order integration scheme is introduced for improved training on sparse and noisy data.Comment: 21 pages, 12 figures; v3: restructured the paper for more clarity, major changes to the text, updated plot

Pseudo-Hamiltonian neural networks with state-dependent external forces

Hybrid machine learning based on Hamiltonian formulations has recently been successfully demonstrated for simple mechanical systems, both energy conserving and not energy conserving. We introduce a pseudo-Hamiltonian formulation that is a generalization of the Hamiltonian formulation via the port-Hamiltonian formulation, and show that pseudo-Hamiltonian neural network models can be used to learn external forces acting on a system. We argue that this property is particularly useful when the external forces are state dependent, in which case it is the pseudo-Hamiltonian structure that facilitates the separation of internal and external forces. Numerical results are provided for a forced and damped mass–spring system and a tank system of higher complexity, and a symmetric fourth-order integration scheme is introduced for improved training on sparse and noisy data.publishedVersio