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Texas A&M University

1. Misra, Navendu. Comparison of motor-based versus visual sensory representations in object recognition tasks.

Degree: MS, Computer Science, 2005, Texas A&M University

Various works have demonstrated the usage of action as a critical component in allowing autonomous agents to learn about objects in the environment. The importance of memory becomes evident when these agents try to learn about complex objects. This necessity primarily stems from the fact that simpler agents behave reactively to stimuli in their attempt to learn about the nature of the object. However, complex objects have the property of giving rise to temporally varying sensory data as the agent interacts with the object. Therefore, reactive behavior becomes a hindrance in learning these complex objects, thus, prompting the need for memory. A straightforward approach to memory, visual memory, is where sensory data is directly represented. Another mechanism is skill-based memory or habit formation. In the latter mechanism the sequence of actions performed for a task is retained. The main hypothesis of this thesis is that since action seems to play an important role in simple perceptual understanding it may also serve as a good memory representation. In order to test this hypothesis a series of comparative tests were carried out to determine the merits of each of these representations. It turns out that skill memory performs significantly better at recognition tasks than visual memory. Furthermore, it was demonstrated in a related experiment that action forms a good intermediate representation of the sensory data. This provides support to theories that propose that various sensory modalities can ideally be represented in terms of action. This thesis successfully extends action to the role of understanding of complex objects. Advisors/Committee Members: Choe, Yoonsuck (advisor), Butler-Purry, Karen L. (committee member), Shipman, Frank (committee member).

Subjects/Keywords: memory; visual; skill; action; object recognition; spatio-temporal pattern

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Misra, N. (2005). Comparison of motor-based versus visual sensory representations in object recognition tasks. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/2544

Chicago Manual of Style (16th Edition):

Misra, Navendu. “Comparison of motor-based versus visual sensory representations in object recognition tasks.” 2005. Masters Thesis, Texas A&M University. Accessed October 31, 2020. http://hdl.handle.net/1969.1/2544.

MLA Handbook (7th Edition):

Misra, Navendu. “Comparison of motor-based versus visual sensory representations in object recognition tasks.” 2005. Web. 31 Oct 2020.

Vancouver:

Misra N. Comparison of motor-based versus visual sensory representations in object recognition tasks. [Internet] [Masters thesis]. Texas A&M University; 2005. [cited 2020 Oct 31]. Available from: http://hdl.handle.net/1969.1/2544.

Council of Science Editors:

Misra N. Comparison of motor-based versus visual sensory representations in object recognition tasks. [Masters Thesis]. Texas A&M University; 2005. Available from: http://hdl.handle.net/1969.1/2544


Delft University of Technology

2. Bosboom, J. Quantifying the quality of coastal morphological predictions.

Degree: 2020, Delft University of Technology

<p class="MsoNormal" style="mso-pagination:none;mso-layout-grid-align:none; text-autospace:none">This thesis investigates the behaviour of the often used point-wise skill score, the MSESSini a.k.a. BSS, and develops new error metrics that, as opposed to point-wise metrics, take the spatial structure of morphological patterns into account. The MSESSini measures the relative accuracy of a morphological prediction over a prediction of zero morphological change, using the mean-squared error (MSE) as the accuracy measure. The main findings about the MSESSini are: 1) a generic ranking, based on values for MSESSini, has limited validity, since the zero change reference model fails to make model performance comparable across different prediction situations; 2) the combination of larger, persistent and smaller, intermittent scales of cumulative change may lead to an increase of skill with time, without the prediction on either of these scales becoming more skilful with time; 3) in the presence of inevitable location errors, the MSESSini favours predictions that underestimate the variance of cumulative bed changes and 4) existing methods to correct for measurement error are inconsistent in either their skill formulation or their suggested classification scheme. In order to overcome the inherent limitations of point-wise metrics, three novel diagnostic tools for the spatial validation of 2D morphological predictions are developed. First, a field deformation or warping method deforms the predictions towards the observations, minimizing the squared point-wise error. Error measures are formulated based on both the smooth displacement field between predictions and observations and the residual point-wise error field after the deformation. In contrast with the RMSE, the method captures the visual closeness of morphological patterns. Second, an optimal transport method defines the distance between predicted and observed morphological fields in terms of an optimal sediment transport field. The optimal corrective transport field moves the misplaced sediment from the predicted to the observed morphology at the lowest quadratic transportation cost. The root-mean-squared value of the optimal transport field, the RMSTE, is proposed as a new error metric. As opposed to the field deformation method, the optimal transport method is mass-conserving, parameter-free and symmetric. The RMSTE, unlike the RMSE, is able to discriminate between predictions that differ in the misplacement distance of predicted morphological features. It also avoids the consistent reward of the underestimation of morphological variability that the RMSE is prone to. Third, a scale-selective validation approach allows any metric to selectively address multiple spatial scales. It employs a smoothing filter in such a way that, in addition to the domain-averaged statistics, localized validation statistics and maps of prediction quality are obtained per scale (geographic extent or areal size of focus). The employed skill score weights how well the morphological structure and… Advisors/Committee Members: Reniers, A.J.H.M., Stive, M.J.F., Delft University of Technology.

Subjects/Keywords: (root)-mean-squared error; model accuracy; morphodynamic modelling; model validation; optimal transport; Monge–Kantorovich; root-mean-squared transport error; effective transport difference; image warping; image matching; scale-selective validation; optical flow; Brier skill score; model skill; zero change model; measurement error; location error; pattern skill

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bosboom, J. (2020). Quantifying the quality of coastal morphological predictions. (Doctoral Dissertation). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; 10.4233/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:isbn:978-94-6384-091-0 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3

Chicago Manual of Style (16th Edition):

Bosboom, J. “Quantifying the quality of coastal morphological predictions.” 2020. Doctoral Dissertation, Delft University of Technology. Accessed October 31, 2020. http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; 10.4233/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:isbn:978-94-6384-091-0 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3.

MLA Handbook (7th Edition):

Bosboom, J. “Quantifying the quality of coastal morphological predictions.” 2020. Web. 31 Oct 2020.

Vancouver:

Bosboom J. Quantifying the quality of coastal morphological predictions. [Internet] [Doctoral dissertation]. Delft University of Technology; 2020. [cited 2020 Oct 31]. Available from: http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; 10.4233/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:isbn:978-94-6384-091-0 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3.

Council of Science Editors:

Bosboom J. Quantifying the quality of coastal morphological predictions. [Doctoral Dissertation]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; 10.4233/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:isbn:978-94-6384-091-0 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3

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