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This thesis introduces the problem of learning the representation and the classification of the perceived sense of movement, defined as dynamism in static visuals. To solve the said problem, we study the definition, degree, and real-world implications of dynamism within the field of consumer psychology. We employ Deep Convolutional Neural Networks (DCNN) as a method to learn and predict dynamism in images. The novelty of the task, lead us to collect a dataset which we synthetically augmented for spatial invariance, using image processing techniques. We study the methods of transfer learning to transfer knowledge from another domain, as the size of our dataset was deemed to be inadequate. Our dataset is trained across different network architectures, and transfer learning techniques to find an optimal method for the task at hand. To show a real-world application of our work, we observe the correlation between the two visual stimuli, dynamism and emotions.

Over last several decades, computer vision researchers have been devoted to find good feature to solve different tasks, object recognition, object detection, object segmentation, activity recognition and so forth. Ideal features transform raw pixel intensity values to a representation in which these computer vision problems are easier to solve. Recently, deep feature from covolutional neural network(CNN) have attracted many researchers to solve many problems in computer vision. In the supervised setting, these hierarchies are trained to solve specific problems by minimizing an objective function for different tasks. More recently, the feature learned from large scale image dataset have been proved to be very effective and generic for many computer vision task. The feature learned from recognition task can be used in the object detection task. This work aims to uncover the principles that lead to these generic feature representations in the transfer learning, which does not need to train the dataset again but transfer the rich feature from CNN learned from ImageNet dataset. This work aims to uncover the principles that lead to these generic feature representations in the transfer learning, which does not need to train the dataset again but transfer the rich feature from CNN learned from ImageNet dataset. We begin by summarize some related prior works, particularly the paper in object recognition, object detection and segmentation. We introduce the deep feature to computer vision task in intelligent transportation system. First, we apply deep feature in object detection task, especially in vehicle detection task. Second, to make fully use of objectness proposals, we apply proposal generator on road marking detection and recognition task. Third, to fully understand the transportation situation, we introduce the deep feature into scene understanding in road. We experiment each task for different public datasets, and prove our framework is robust.

This work examines the representations of the Perennial Philosophy in the literature of the Deep Ecology movement, and the negative response of critics to the Self-realisation approach. It then goes on to suggest that a deeper engagement with the nondualistic doctrines Naess embraced could lift environmental philosophy out of the Cartesian framework in which it appears to be bogged down. Deep Ecology has been accused of being politically ineffective, and letting down the environmental movement, because it remains insufficiently engaged with debates concerning power, class, sex, and other hegemonies that occupy the minds of social ecologists, ecofeminists, and cultural studies theorists. I argue that Deep Ecology is not as ineffective as detractors claim, but that it remains philosophically undeveloped, and has not provided sound foundations for environmental ethics. The qualified nondualism I advance, based on Ved??nta, the work of David Bohm, and (to a lesser extent) Platonic thought, treats cosmos, society and the individual as intelligent creative systems in which the interrelated parts are expressions of a vital generative order to which each is actively related. The Self is a mirror of the cosmos, engaged in the process of becoming a more complete reflection of the totality. In all of this the nature of consciousness as vast creative intelligence is paramount, and freedom dominates the entire process from beginning to end. This thesis offers an opportunity to rethink ideas of value, moral considerability, and the nature of the empirical self, from a nondualistic perspective. It proposes that "intrinsic unity" might replace the community as the foundational moral concept for environmental ethics. In the process, emphasis shifts away from the objective sphere and settles firmly on the thinker and thought. Following Bohm and Krishnamurti, I argue that conditioned thought is the only barrier to (inner) freedom and creativity. Most important, the metaphysics of nondualism privileges processes of universal Self-realisation, and reveals the limitations of the empirical self. Understanding thought as a process then becomes something of a moral imperative.

The ecological forces that drive community structure of deep-sea benthic communities are poorly understood, yet such communities rival in biological complexity those of coral reefs or rainforests. Using components of the recently concluded DGoMB project, local and regional-scale structure of benthic macrofaunal communities were examined at thirty two locations throughout the continental slope of the northern Gulf of Mexico. Controlling factors associated with sediment disturbance, food supply, and faunal competition between functional ecological groups were evaluated for correlative and relational patterns. A higher order taxonomic sufficiency approach was used to calculate both alpha and beta diversity. The results of this study indicate that macrofaunal communities are very patchy, having wide variations in abundance at within-site, adjacent-site, and across-basin scales, yet all sample areas possess a large richness of higher taxa. Declining abundance was noted with increasing water depth and reduced particulate organic carbon levels. Upper-slope submarine canyons possess some of the highest abundances. Less mobile macrofauna, such as poriferans, bivalves, and scaphopods, dominate slope communities above the 500 meter contour. Sediments exhibiting intense megafaunal bioturbation inhibit abundances of sedentary macrofaunal taxa, but such mixing is positively associated with increased abundances of polychaetes and ambulatory crustaceans, including peracarids, harpacticoids, and ostracods. Prominent sediment mixing was noted at most sites, including portions of the Sigsbee Abyssal Plain. The western Gulf of Mexico was less biologically active than the eastern Gulf of Mexico, which possesses two extensive submarine canyons that appear to act as regional nutrient traps. I conclude that the physiographic complexity of the northern Gulf of Mexico continental slope influences macrofaunal community structure. Biological disturbance, in the form of sediment mixing, is widespread throughout most slope depths, and the benthic environment is food-limited. It appears that disequilibrium-type ecological processes predominate in this area, supporting similar findings by previous studies in other regions of the ocean, usually at far smaller scales and none representative at the basin-level. Use of higher order taxonomy in lieu of genus or species-level faunal identifications for diversity measurements was inadequate for detecting spatial patterns or environmental responses.

In this study, a vector form intrinsic finite element (VFIFE) is derived and applied to study both the static and dynamic responses of deep short beams under dynamic loadings. It is already known that the application of classical beam theory known as Euler¡¦s beam theory to beams with large ratio of D/L (depth/span larger than 1/4), a short-deep beam, may not necessarily obtain satisfactory results for the stress analysis of the beam. One of the main presumptions from the classical Euler¡¦s beam theory is that the plane of the cross-section remains plane and normal to the neutral axis of the beam after deformation. This presumption is no more true when the beam subject to loadings is a short-deep beam because the bending stress is no longer a dominant stress while the other secondary effects may have more severe influences on the mechanical behavior of the beam. This study by utilizing the vector form intrinsic finite element method (VFIFE) to derive a new element for the Timoshenko beam provides an alternative method for the analysis of a short-deep beam, particularly, subject to dynamic loadings. By taking the advantage of the VFIFE that is a time-saving scheme for the dynamic analysis, the element of Timoshenko-beam is derived along with the dynamic solution procedure. The motions in transverse direction and the rotation at each node of the beam are calculated and presented into figures. The results from numerical analysis are also verified with theoretical solution (exact analytical solution) and further compared to the results obtained from traditional finite element method.

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