An evaluation of the Amblyopia and Strabismus Questionnaire using Rasch analysis

noPURPOSE. To evaluate whether the Amblyopia and Strabismus Questionnaire (A&SQ) is a suitable instrument for the assessment of vision-related quality-of life (VR-QoL) in individuals with strabismus and/or amblyopia. METHODS. The A&SQ was completed by 102 individuals, all of whom had amblyopia, strabismus, or both. Rasch analysis was used to evaluate the usefulness of individual questionnaire items (i.e., questions); the response-scale performance; how well the items targeted VR-QoL; whether individual items showed response bias, depending on factors such as whether strabismus was present; and dimensionality. RESULTS. Items relating to concerns about the appearance of the eyes were applicable only to those with strabismus, and many items showed large ceiling effects. The response scale showed disordered responses and underused response options, which improved after the number of response options was reduced from five to three. This change improved the discriminative ability of the questionnaire (person separation index increased from 1.98 to 2.11). Significant bias was found between strabismic and nonstrabismic respondents. Separate Rasch analyses conducted for subjects with and without strabismus indicated that all A&SQ items seemed appropriate for individuals with strabismus (Rasch infit values between 0.60 and 1.40), but several items fitted the model poorly in amblyopes without strabismus. The AS&Q was not found to be unidimensional. CONCLUSIONS. The findings highlight the limitations of the A&SQ instrument in the assessment of VR-QoL in subjects with strabismus and especially in those with amblyopia alone. The results suggest that separate instruments are needed to quantify VR-QoL in amblyopes with and without strabismus

Implicit Gradient Regularization

Gradient descent can be surprisingly good at optimizing deep neural networks without overfitting and without explicit regularization. We find that the discrete steps of gradient descent implicitly regularize models by penalizing gradient descent trajectories that have large loss gradients. We call this Implicit Gradient Regularization (IGR) and we use backward error analysis to calculate the size of this regularization. We confirm empirically that implicit gradient regularization biases gradient descent toward flat minima, where test errors are small and solutions are robust to noisy parameter perturbations. Furthermore, we demonstrate that the implicit gradient regularization term can be used as an explicit regularizer, allowing us to control this gradient regularization directly. More broadly, our work indicates that backward error analysis is a useful theoretical approach to the perennial question of how learning rate, model size, and parameter regularization interact to determine the properties of overparameterized models optimized with gradient descent

Time Course of Altered Sensitivity to Inhibitory and Excitatory Agonist Responses in the Longitudinal Muscle–Myenteric Plexus and Analgesia in the Guinea Pig after Chronic Morphine Treatment

Tolerance that develops after chronic morphine exposure has been proposed to be an adaptive response that develops and decays over a defined time course. The present study examined the development of tolerance to the acute hypothermic and analgesic effects of morphine and correlated the time course for the desensitization in vivo with the reduced responsiveness to DAMGO and 2-CADO and increased responsiveness to nicotine of the longitudinal muscle/myenteric plexus (LM/MP) preparation in vitro. Assessment was performed at various times after morphine or placebo pellet implantation. Morphine produced a modest hypothermic response to which no tolerance developed. However, the development of tolerance to the analgesic effect of morphine, the inhibitory effect of DAMGO and CADO on neurogenic twitches of the LM/MP and hypersensitivity to the contractile response to nicotine was observed to occur in a time-dependent manner. The alterations in sensitivity to DAMGO, nicotine, and responsiveness to morphine analgesia occurred between days 4 and 10 and returned to normal by day 14 post-implantation. In contrast, sensitivity of LM/MP preparations to 2-CADO displayed a similar time-dependent onset but the tolerance persisted beyond 14 days after implantation. These data suggest that the heterologous tolerance that develops after chronic morphine treatment is time-dependent and persistent but, ultimately returns to normal in the absence of any intervention. Furthermore, the data suggest that the basis of the adaptive phenomenon may involve multiple cellular mechanisms including the modulation of cell excitability and normal physiology but the consequences of the adaptation extend to all effects of the agonist

Distinct Quantum States Can Be Compatible with a Single State of Reality

Perhaps the quantum state represents information about reality, and not reality directly. Wave function collapse is then possibly no more mysterious than a Bayesian update of a probability distribution given new data. We consider models for quantum systems with measurement outcomes determined by an underlying physical state of the system but where several quantum states are consistent with a single underlying state---i.e., probability distributions for distinct quantum states overlap. Significantly, we demonstrate by example that additional assumptions are always necessary to rule out such a model.Comment: 5 pages, 2 figure

Why neural networks find simple solutions: the many regularizers of geometric complexity

In many contexts, simpler models are preferable to more complex models and the control of this model complexity is the goal for many methods in machine learning such as regularization, hyperparameter tuning and architecture design. In deep learning, it has been difficult to understand the underlying mechanisms of complexity control, since many traditional measures are not naturally suitable for deep neural networks. Here we develop the notion of geometric complexity, which is a measure of the variability of the model function, computed using a discrete Dirichlet energy. Using a combination of theoretical arguments and empirical results, we show that many common training heuristics such as parameter norm regularization, spectral norm regularization, flatness regularization, implicit gradient regularization, noise regularization and the choice of parameter initialization all act to control geometric complexity, providing a unifying framework in which to characterize the behavior of deep learning models.Comment: Accepted as a NeurIPS 2022 pape